In the grand scheme of things, it’s becoming increasingly evident that data is the new black gold. Industries across the board are awakening to the realization that data is no longer just an afterthought or an add-on; it’s an essential component of success. In the 19th century, oil was the lifeblood of the global economy and politics. In the 21st century, data is controlled to take on the same critical role.

Of course, data in its raw and unrefined form is essentially useless. It’s only when data is skillfully gathered, integrated, and analyzed that it starts to unlock its actual value. This value can manifest in many ways, from enhancing decision-making capabilities to enabling entirely new business models. In the healthcare industry, data is playing a particularly pivotal role. Refined data is helping professionals make better-informed decisions, improve patient outcomes, and unlock new frontiers of medical research. The future of healthcare is all about data, and those who know how to wield it will undoubtedly emerge as leaders in the field.

However, healthcare providers’ timely access to real-time or just-in-time information can significantly enhance patient care, optimize clinician efficiency, streamline workflows, and reduce healthcare costs.

Investing in robust electronic health record (EHR) systems encompassing all clinical data is crucial for healthcare organizations to understand patient conditions and comprehensively predict patient outcomes.

Is Data a Real Game Changer in the Healthcare Industry?

The answer to whether the analytical application of existing data will shape the future of healthcare is a resounding “yes.” With advances in data-collecting tools and healthcare technology, we’re witnessing a new era of healthcare delivery that will revolutionize the industry.

Imagine a world where wearable medical devices warn you of potential health risks or medical advice apps offer personalized guidance based on your unique DNA profile. These are just a few examples of how cutting-edge technology is making its way into the healthcare space, enabling data-driven decisions that improve patient outcomes and drive down costs.

Real-time data is a game-changer for case review and clinical time management, allowing healthcare professionals to understand patient situations and forecast outcomes more effectively. To fully realize the potential of data-driven healthcare, healthcare organizations must implement robust data management systems that can store all clinical data and provide the necessary tools for data analysis. By doing so, healthcare professionals will be empowered to make informed decisions that enhance patient care, improve outcomes, and ultimately transform the healthcare landscape.

Also, read the best approach to testing digital healthcare.

How do you use data for a better future?

When it comes to healthcare, data is everything. However, with the massive amounts of data that healthcare professionals must contend with, the sheer volume of information can be overwhelming.
As the industry has shifted toward electronic record keeping, healthcare organizations have had to allocate more resources to purchasing servers and computing power to handle the influx of data. This has led to a significant surge in spending across the sector.

Despite the clear advantages of data-driven healthcare, managing such large amounts of information presents unique challenges. Sorting through and making sense of the data requires robust data management systems and advanced analytical tools. However, with the right approach, healthcare professionals can leverage this data to make informed decisions that improve patient outcomes and transform the industry.

How does data analytics benefit the healthcare industry?

A small diagnostic error can have devastating consequences in the healthcare industry, potentially costing lives. The difference between an actual positive malignant tumor and a benign one can be the difference between life and death. This is where data analytics comes into play, helping to eliminate the potential for error by identifying the most relevant patterns in the available data and predicting the best possible outcome.

Beyond improving patient care, data analytics can also assist hospital administration in evaluating the effectiveness of their medical personnel and treatment processes. As the industry continues to shift toward providing high-quality and reasonable care, the insights derived from data analysis can help organizations stay on the cutting edge of patient care.

With data analytics, healthcare professionals can harness the power of big data to identify patterns and trends, predict patient outcomes, and improve the overall quality of care. Healthcare organizations can optimize their processes by leveraging data-driven insights, minimizing errors, and ultimately delivering better patient outcomes.

Approaches of Data Analytics

Data analytics is a complex process involving various approaches, E.g., predictive analysis, descriptive analysis, and prescriptive analysis, including feature understanding, selection, cleaning, wrangling, and transformation. These techniques are applied depending on the type of data being analyzed.

Analysts must first understand the features and variables relevant to the analysis to derive insights from the data. From there, they can select the most relevant features and begin cleaning and wrangling the data to ensure accuracy and completeness.

Once the data has been prepared, analysts can apply various transformation techniques to derive insights and patterns. The specific methods used will depend on the nature of the data being analyzed but may include methods such as regression analysis, clustering, and decision trees.

Predictive Analysis

Analysts leverage sophisticated techniques such as relational, dimensional, and entity-relationship analysis methodologies to forecast outcomes. By applying these powerful analytical methods, they can extract insights from large and complex datasets, identifying patterns and relationships that might otherwise be obscured.

Whether analyzing patient data to forecast disease progression or studying market trends to predict demand for new medical products, these advanced analytical techniques are essential for making informed decisions in today’s data-driven world. By leveraging the latest tools and techniques, healthcare professionals can stay ahead of the curve, improving patient outcomes and driving innovation in the industry.

Descriptive Analysis

In the data analytics process, descriptive analysis is a powerful technique that can be used to identify trends and patterns in large datasets. Unlike more complex analytical methods, descriptive analysis relies on simple arithmetic and statistics to extract insights from the data.

Analysts can gain a deeper understanding of data distribution by analyzing descriptive statistics such as mean, median, and mode, helping to identify common trends and patterns. This information is invaluable during the data mining phase, assisting analysts to uncover hidden insights and identify opportunities for further analysis.

Prescriptive Analysis

In data analytics, prescriptive analysis represents the pinnacle of analytical techniques. Beyond simple descriptive or predictive analysis, prescriptive analysis offers recommendations for proceeding based on insights gleaned from the data.

This highly advanced analysis is the key to unlocking new opportunities in the healthcare industry, enabling professionals to make more informed decisions about everything from treatment protocols to resource allocation. By leveraging sophisticated algorithms and machine learning techniques, prescriptive analysis can identify the optimal path forward for any situation, helping organizations optimize processes, maximize efficiency, and drive better patient outcomes.

Gathering Real-time Data in Healthcare

Real-time data refers to data that is immediately obtained upon its creation and can be collected using various methods, including:

• Health Records
• Prescriptions
• Diagnostics Data
• Apps and IoTs

Real-time data is crucial for managing the healthcare industry’s patient care, operations, and staffing routines. By leveraging real-time data, the industry can optimize its entire IT infrastructure, gaining greater insight and understanding of its complex networks.

Examples of Real-time Data Technologies in Healthcare

Role of AI/ML in healthcare

Regarding medical diagnostics, the power of data analytics cannot be overstated. Thanks to cutting-edge machine learning and deep learning methods, it’s now possible to analyze medical records and predict future outcomes with unprecedented precision.

Take machine learning, for example. By leveraging this technology, medical practitioners can reduce the risk of human error in the diagnosis process while also gaining new insights into graphic and picture data that could help improve accuracy. Additionally, analyzing healthcare consumption data using machine learning algorithms makes it possible to allocate resources more effectively and reduce waste.

But that’s not all. Deep learning is also a game-changer in the fight against cancer. Researchers have achieved remarkable results by training a model to recognize cancer cells using deep neural networks. By feeding the model a wealth of cancer cell images, it could “memorize” their appearance and use that knowledge to detect cancerous cells in future images accurately. The potential for this technology to save lives is truly staggering.

RPA (Robotic process automation) in healthcare

The potential for RPA in healthcare is fascinating. By scanning incoming data and scheduling appointments based on a range of criteria like symptoms, suspected diagnosis, doctor availability, and location, RPA can dramatically boost efficiency. This would relieve the burden of time-consuming scheduling tasks from the healthcare staff and probably improve patient satisfaction.

In addition to appointment scheduling, RPA can also be used to speed up health payment settlements. By consolidating charges for different services, including testing, medications, food, and doctor fees, into a single, more straightforward payment, healthcare practitioners can save time and avoid billing errors. Plus, if there are any issues with cost or delays, RPA can be set up to email patients with customized reminders.

But perhaps the most exciting use of RPA in healthcare is data analysis. By leveraging this technology to produce insightful analytics tailored to each patient’s needs, healthcare providers can deliver more precise diagnoses and treatment plans. Ultimately, this can lead to better outcomes and an enhanced patient care experience.

Role of Big Data in Healthcare

In today’s world, the healthcare industry needs an innovation that can empower medical practitioners to make informed decisions and ultimately enhance patient outcomes. Big data is the transformative force that can revolutionize how we approach healthcare. With the ability to analyze massive amounts of data from various sources, big data can provide medical practitioners with the insights they need to understand better and treat diseases. By leveraging this data, doctors can develop more targeted treatments and therapies that have the potential to improve patient outcomes drastically.

Beyond the immediate benefits of improved treatment options, big data also plays a vital role in driving new drug development. Through advanced clinical research analysis, big data can predict the efficacy of potential new drugs, making it easier for scientists to identify the most promising candidates for further development. This is just one example of how big data is revolutionizing the way we approach healthcare, and the benefits will only continue to grow as we explore more ways to harness its power.

Finally, big data is helping healthcare practitioners to create focused treatments that are tailored to improve population health. By analyzing population health data, big data can detect patterns and trends that would be impossible to identify through other means. With this information, medical professionals can develop targeted treatments that can be applied on a large scale, ultimately improving health outcomes for entire populations. This is just one of the many ways that big data is changing the way we approach healthcare, and it’s clear that the possibilities are endless. As we continue to explore this transformative technology, there’s no doubt that we’ll discover even more innovative ways to leverage big data to improve health outcomes for patients around the world.

Wrapping Up

In conclusion, real-time data analysis is a transformative force in the healthcare industry that has the potential to revolutionize the way we approach patient care. With the ability to analyze vast amounts of data in real-time, medical practitioners can make faster and more informed decisions, resulting in improved patient outcomes and ultimately saving lives.

From predicting potential health risks to identifying disease outbreaks and monitoring patient progress, real-time data analysis is driving innovation in healthcare and changing the way medical professionals approach treatment. By leveraging cutting-edge technologies and advanced analytics tools, healthcare organizations can collect and analyze data from various sources, including wearable devices, electronic health records, and social media, to better understand patient needs and provide personalized care.

As the healthcare industry continues to evolve, it’s clear that real-time data analysis will play an increasingly important role in delivering better health outcomes for patients worldwide. Real-time data analysis can improve patient care, reduce costs, and save lives by giving medical practitioners the insights they need to make more informed decisions. The possibilities for the future of healthcare services are endless, and I’m excited to see the continued innovations that will arise from this transformative technology.

The post Real-Time Data Analysis and its Impact on Healthcare appeared first on Indium.

In the first part of our exploration, we laid the foundation for evaluating NLP models in the financial landscape. We emphasized the critical role of high-quality datasets and dived into the capabilities of foundational NLP models, particularly distilbert-base-uncased-finetuned-sst-2-english. Understanding the importance of data selection and model choice forms the bedrock for our deeper dive into specialized models tailored for financial analysis.

Comparative Evaluation

We can compare the results of all 3 models against each other to determine which one performs better. The metrics used are Accuracy, precision, recall and F1 score. Accuracy, recall, precision, and F1 score are common performance metrics used to evaluate the performance of classification models, such as in natural language processing tasks like sentiment analysis or text categorization. Each metric provides insights into different aspects of the model’s predictions.

Accuracy is the most basic evaluation metric and represents the overall correctness of the model’s predictions. It is calculated as the ratio of correctly predicted instances (true positives and true negatives) to the total number of instances in the dataset. While accuracy is a useful metric, it can be misleading when dealing with imbalanced datasets where one class dominates the others, leading to high accuracy even if the model performs poorly on the minority class.

Recall, also known as sensitivity or true positive rate, measures the ability of the model to correctly identify all positive instances (true positives) out of all the actual positive instances (true positives + false negatives). It gives insights into the model’s ability to avoid false negatives and capture relevant positive instances. A high recall indicates that the model is effective at identifying positive cases, even if it means having more false positives.

Precision measures the accuracy of the model’s positive predictions by calculating the ratio of true positives to the sum of true positives and false positives. It shows how well the model avoids false positives. A high precision indicates that the model is conservative in its positive predictions and minimizes false alarms.

The F1 score is the harmonic mean of precision and recall and is used to balance both metrics. It provides a single metric that combines precision and recall, allowing a more comprehensive evaluation of the model’s performance. The F1 score is particularly useful in cases where both high precision and high recall are desired, as it penalizes models that prioritize one metric over the other. A higher F1 score indicates a better balance between precision and recall.

Evaluating each model, it is clear that all 3 have a very good true positive rate, resulting in high precision, recall and F1 score across the board. However, DistilBERT has a very low accuracy on the financial dataset while FINBERT has a very low accuracy in the IMDB dataset. This effectively demonstrates the generalization capability of the BERT model when it is not finetuned for any specific domain. It also demonstrates the capability of FINBERT to perform well with financial data as well as its inability to generalize to non-financial data.

FINBERT-tone however, is an entirely different case. It appears to have an increased generalizing capability than its non-fine-tuned variant as demonstrated by its performance on the IMDB dataset. However, it is not as capable as FINBERT when classifying, this is in direct contradiction of the claims of the developers on the hugging face platform who claimed that this model would be more capable than FINBERT for sentiment analysis tasks. This may be attributed to many areas within the methodology. However, it is likely that FinBERT-tone is much more sensitive to tone, and this may have resulted in its inability to perform a simple binary classification. As the ability to gauge nuance increases, the model has deviated from seeing in black and white.

It is important to note, however, that in the evaluation process, we have ignored the neutral labels in the financial sentiment dataset and have ignored the neutral classifications of the models that have a neutral class. Thus, the data may be significantly skewed based on the model’s capability to handle neutral sentiment.

Summarization

bart-large-cnn

Link: https://huggingface.co/facebook/bart-large-cnn

BART (Bidirectional and Auto-Regressive Transformers) is a state-of-the-art language generation model introduced by Facebook AI Research (FAIR). Unlike traditional transformer models that are primarily designed for tasks like language understanding, BART excels in both text generation and comprehension. The model employs a two-step process, consisting of bidirectional pre-training and auto-regressive decoding. During pre-training, BART learns to predict masked words in a bidirectional manner, similar to BERT. However, it also utilizes an auto-regressive decoder to predict subsequent words, enabling it to generate coherent and contextually relevant text.

One of BART’s key strengths lies in its ability to perform various text generation tasks, such as text summarization, machine translation, and question answering, by fine-tuning the pre-trained model on specific datasets. Its auto-regressive nature allows it to generate lengthy and coherent responses, making it particularly effective for tasks requiring context-aware language generation. BART has demonstrated exceptional performance in various natural language processing tasks and has quickly become a popular choice among researchers and developers for its versatility and ability to handle both text comprehension and generation with impressive results.

The BART-large model has 400 million parameters. It contains 12 layers on the encoder and decoder side with 16 attention heads. It has a vocab size of 50264 and takes a maximum input length of 1024.

Choosing BART can be a highly advantageous decision due to its remarkable versatility and prowess in both text comprehension and generation tasks. As a bidirectional and auto-regressive transformer model, BART combines the strengths of pre-training with bidirectional context understanding, similar to BERT, and auto-regressive decoding to generate coherent and contextually relevant text. This unique architecture enables BART to excel in a wide range of natural language processing tasks, such as text summarization, machine translation, and question answering.

More information: https://github.csom/facebookresearch/fairseq/tree/main/examples/bart

Code snippet:

import pandas as pd

import torch

import transformers

from transformers import pipeline

df = pd.read_csv(dataset path)

df.head()

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained(“facebook/bart-large-cnn”, max_length = 1024)

model = AutoModelForSeq2SeqLM.from_pretrained(“facebook/bart-large-cnn”)

nlp = pipeline(“summarization”, model=model, tokenizer=tokenizer, device = 0)

X = df.iloc[:,full text column]

y = df.iloc[:,summary column]

!pip install torchmetrics

from torchmetrics.text.rouge import ROUGEScore

from transformers import pipeline

torch.cuda.set_device(0)

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)

rouge = ROUGEScore()

rougeL = 0

rouge1 = 0

rouge2 = 0

count = 0

wrong_dict = {}

for input_sequence in X:

  try:

    tokenized = tokenizer(input_sequence, max_length = 1024, return_tensors = ‘pt’).to(device)

    summary_ids = model.generate(tokenized[“input_ids”], num_beams=2)

    summary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]

    # results = nlp(input_sequence)

    # summary = results[0][‘summary_text’]

    rougeL += rouge(summary, y[count])[‘rougeL_fmeasure’].item()

    rouge1 += rouge(summary, y[count])[‘rouge1_fmeasure’].item()

    rouge2 += rouge(summary, y[count])[‘rouge2_fmeasure’].item()

except:

    pass

  count += 1

  print(count,’/2000 complete’, end = ‘\r’)

  # if count == 2000:

  #   break

rouge1_score = rouge1/count

rouge2_score = rouge2/count

rougeL_score = rougeL/count

print(‘\nRougeL fmeasure:’, rougeL_score)

print(‘Rouge1 fmeasure:’, rouge1_score)

print(‘Rouge2 fmeasure:’, rouge2_score)

Financial Summarization-PEGASUS

Link: https://huggingface.co/human-centered-summarization/financial-summarization-pegasus

PEGASUS is an advanced language model developed by Google Research, known for its exceptional capabilities in abstractive text summarization. Unlike extractive summarization, where sentences are selected from the original text, PEGASUS generates concise and coherent summaries by paraphrasing and reorganizing the content. The model’s architecture is built upon the Transformer-based encoder-decoder framework, and it is trained on a large corpus of diverse data to develop a deep understanding of language semantics and coherence.

One of PEGASUS’s key strengths lies in its ability to produce informative and contextually accurate summaries across various domains and languages. By leveraging pre-training and fine-tuning techniques, PEGASUS can be tailored to specific summarization tasks, achieving remarkable performance in summarizing long documents, news articles, and other text types. Its remarkable generalization abilities make it a valuable tool for generating high-quality summaries in scenarios where human-like summarization is essential, such as content curation, document analysis, and information retrieval.

This fine-tuned model of PEGASUS claims to have an improved performance for financial summarization. It contains 16 layers in the encoder and decoder and takes a maximum input length of 512 tokens. The vocab size is 96103, however, the summary length is much shorter than BART.

Selecting PEGASUS can be a highly advantageous decision for tasks requiring abstractive text summarization. Its exceptional capabilities in generating coherent and informative summaries make it an invaluable asset in various domains. Unlike extractive summarization approaches, PEGASUS excels in paraphrasing and reorganizing content, enabling it to produce concise and contextually accurate summaries that capture the essence of the original text.

PEGASUS’s Transformer-based encoder-decoder architecture, combined with extensive pre-training on diverse datasets, equips it with a deep understanding of language semantics and coherence. This extensive training empowers PEGASUS to generalize effectively across different domains and languages, ensuring its performance remains robust and reliable. From summarizing long documents to news articles and more, PEGASUS can be fine-tuned to tailor its summarization abilities to specific tasks, making it an ideal choice for applications that demand human-like summarization quality, such as content curation, document analysis, and knowledge extraction. In summary, PEGASUS’s proficiency in abstractive summarization and its adaptability across diverse domains make it a compelling and powerful choice for tasks that require top-notch language understanding and summarization capabilities.

Code snippet:

!pip install sentencepiece

import sentencepiece as sentencepiece

from transformers import PegasusTokenizer, PegasusForConditionalGeneration

model_name = “human-centered-summarization/financial-summarization-pegasus”

tokenizer = PegasusTokenizer.from_pretrained(model_name)

model = PegasusForConditionalGeneration.from_pretrained(model_name)

import pandas as pd

#bitcoin articles dataset

df = pd.read_csv(dataset path)

df.head()

from transformers import pipeline

nlp = pipeline(“summarization”, model=model, tokenizer=tokenizer, device = 0, max_length=80, min_length=50)

!pip install torchmetrics

from torchmetrics.text.rouge import ROUGEScore

import torch

torch.cuda.set_device(0)

device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)

rouge = ROUGEScore()

from pprint import pprint

print(nlp([X[0]])[0][‘summary_text’])

#code for getting metrics on dataset

rougeL = 0

rouge1 = 0

rouge2 = 0

count = 0

wrong_dict = {}

for input_sequence in X:

  try:

    summary = nlp(input_sequence)[0][‘summary_text’]

    rougeL += rouge(summary, y[count])[‘rougeL_fmeasure’].item()

    rouge1 += rouge(summary, y[count])[‘rouge1_fmeasure’].item()

    rouge2 += rouge(summary, y[count])[‘rouge2_fmeasure’].item()

  except:

    pass

  count += 1

  print(count,’/2000 complete’, end = ‘\r’)

  # if count == 5:

  #   break

rouge1_score = rouge1/count

rouge2_score = rouge2/count

rougeL_score = rougeL/count

print(‘\nRougeL fmeasure:’, rougeL_score)

print(‘Rouge1 fmeasure:’, rouge1_score)

print(‘Rouge2 fmeasure:’, rouge2_score)

Comparative Evaluation

We can compare the results of both models against each other to determine which one performs better. The metric used for this was ROUGE score fmeasure. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics commonly used to evaluate the quality of automatic text summarization. Its primary focus is on measuring the similarity between the generated summary and one or more reference summaries created by humans. ROUGE calculates various metrics, including ROUGE-N, ROUGE-L, and ROUGE-W, each evaluating different aspects of summarization quality.

ROUGE-N measures the n-gram overlap between the generated summary and the reference summary, where “N” represents the number of consecutive words in the n-gram. ROUGE-L, on the other hand, evaluates the longest common subsequence between the generated and reference summaries, considering not only individual words but also the order in which they appear. Lastly, ROUGE-W extends the evaluation to weighted word sequences, accounting for the importance of words in the summaries based on their frequency in the reference summaries.

ROUGE scores are widely used in research and development of automatic summarization systems, as they provide objective and quantitative measures to assess the quality of generated summaries. Higher ROUGE scores indicate better similarity between the generated summary and the human-created references, suggesting that the summarization system produces summaries that capture the essential content and structure of the original text more effectively. However, ROUGE scores should be interpreted alongside other metrics and human evaluation to ensure a comprehensive assessment of the summarization system’s performance.

ROUGE F-measure, often referred to as ROUGE-F1, is a commonly used evaluation metric in automatic text summarization tasks. It is a combination of precision and recall and is calculated as the harmonic mean of these two metrics.

Precision measures the proportion of words in the generated summary that also appear in the reference summary. It represents the ability of the summarization system to avoid producing irrelevant words that do not appear in the human-created reference summary. Recall, on the other hand, measures the proportion of words in the reference summary that are also present in the generated summary. It represents the ability of the summarization system to capture important information from the original text. By taking the harmonic mean of precision and recall, the ROUGE F-measure balances both metrics and provides a single score that evaluates the overall performance of the summarization system. A higher ROUGE F-measure indicates a better balance between precision and recall, suggesting that the summarization system produces summaries that are both concise and comprehensive, capturing the relevant content from the original text effectively.

From the result analysis, it is evident that the BART model outperforms the PEGASUS model. We can attribute this to many factors including the fact that BART can handle a longer token length, making it easier for the model to handle longer dependencies. It may also be due to the training methods each model has been developed with. Or, we can attribute this large variation to BART’s architecture and the advantage of using an autoregressive decoder. Nonetheless, it is clear that BART is the preferred model regardless of what data it is summarizing.

Conclusion

Upon conducting a thorough evaluation of all the models on two distinct datasets, the findings provide robust and well-justified conclusions that bear significant implications for text classification and summarization tasks.

For text classification, the results unambiguously point to FINBERT as the top-performing model. Its exceptional performance in handling financial text data showcases its specialization and domain-specific expertise, making it the ideal choice for financial sentiment analysis. While FINBERT-tone claimed to outperform the base model, this could not be substantiated by the evaluation, raising questions about its purported advantages in text classification tasks. Furthermore, the evaluation demonstrates that DistilBERT and, by extension the BERT base model, exhibit remarkable performance on more general datasets, illustrating their versatility and adaptability to various text classification challenges, including financial data analysis.

Moving to the task of summarization, the evaluation decisively positions BART as the clear winner. Its superior performance across both general and domain-specific datasets sets it apart from other models, including PEGASUS. BART’s abstractive summarization capabilities allow it to generate coherent and informative summaries that capture the essence of the original text, making it the preferred choice for summarization both general and domain specific. Despite its competence, the evaluation indicates that PEGASUS could not contend with BART’s performance in summarization tasks.

In conclusion, the evidence-based conclusions drawn from the rigorous evaluation provide valuable insights for selecting the most suitable models for text classification and summarization tasks. FINBERT shines as the optimal choice for text classification, particularly in financial domains, while BART emerges as the superior model for summarization, showcasing its capabilities in producing accurate and contextually rich summaries. These findings contribute to advancing the understanding of NLP model performance, guiding practitioners, and researchers in making informed decisions, and elevating the effectiveness of NLP applications in diverse real-world scenarios.

The post Evaluating NLP Models for Text Classification and Summarization Tasks in the Financial Landscape part 2 appeared first on Indium.

The financial landscape is an intricate ecosystem, where vast amounts of textual data carry invaluable insights that can influence markets and shape investment decisions. With the rise of Natural Language Processing (NLP) technologies, the financial industry has found a potent ally in processing, comprehending, and extracting actionable intelligence from this wealth of textual information. In pursuit of harnessing the potential of cutting-edge NLP models, this research endeavor embarked on a meticulous evaluation of various NLP models available on the Hugging Face platform. The primary objective was to assess their performance in financial text classification and summarization tasks, two essential pillars of efficient data analysis in the financial domain.

Financial text classification is a critical aspect of sentiment analysis, topic categorization, and predicting market movements. In parallel, summarization techniques hold paramount significance in digesting extensive texts, capturing salient information, and facilitating prompt decision-making in a rapidly evolving market landscape.

To undertake this comprehensive assessment, two appropriate datasets were chosen to assess models for both summarization and classification tasks. For summarization, the datasets selected were the CNN Dailymail dataset to evaluate the models’ capabilities with more general data, and a dataset of bitcoin-related articles to assess the models’ capabilities with finance-related data. For classification, the datasets selected were a dataset of IMDB reviews, and a dataset of financial documents from a variety of different sectors within the financial industry.

The chosen models for this study were:

distilbert-base-uncased-finetuned-sst-2-english

finbert

finbert-tone

bart-large-cnn

financial-summarization-pegasus

These models were obtained from the Hugging Face platform. Hugging Face is a renowned platform that has emerged as a trailblazer in the realm of Natural Language Processing (NLP). At its core, the platform is dedicated to providing a wealth of resources and tools that empower researchers, developers, and NLP enthusiasts to explore, experiment, and innovate in the field of language understanding. Hugging Face offers a vast repository of pre-trained NLP models that have been fine-tuned for a wide range of NLP tasks, enabling users to leverage cutting-edge language models without the need for extensive training. This accessibility has expedited NLP research and development, facilitating the creation of advanced language-based applications and solutions. Moreover, Hugging Face fosters a collaborative environment, encouraging knowledge sharing and community engagement through discussion forums and support networks. Its user-friendly API and open-source libraries further streamline the integration of NLP capabilities into various projects, making sophisticated language processing techniques more accessible and applicable across diverse industries and use cases.

Gathering the Datasets

In the domain of data-driven technologies, the age-old adage “garbage in, garbage out” holds more truth than ever. At the heart of any successful data-driven endeavor lies the foundation of a high-quality dataset. A good dataset forms the bedrock upon which algorithms, models, and analyses rest, playing a pivotal role in shaping the accuracy, reliability, and effectiveness of any data-driven system. Whether it be in the domains of machine learning, artificial intelligence, or statistical analysis, the quality and relevance of the dataset directly influence the outcomes and insights derived from it. Thus, to evaluate the chosen models, it was imperative that the right datasets were chosen. The datasets used in this study were gathered from Kaggle.

For classification, the chosen neutral dataset was the IMDB Movie Review dataset, which contains 50,000 movie reviews and an assigned sentiment score. You can access it here. As for the financial text dataset, the selected dataset was the Financial Sentiment Analysis dataset, comprising over 5,000 financial records with assigned sentiments. You can find it here. It was necessary to remove the neutral values since not all the selected models have a neutral class.

For summarization, the neutral dataset chosen was the CNN Dailymail dataset, which contains 30,000 news articles written by CNN and The Daily Mail. Only the test dataset was utilized for this evaluation, which includes 11,490 articles and their summaries. You can access it here. For the financial text dataset, the Bitcoin – News articles text corpora dataset was used. This dataset encompasses numerous articles about bitcoin gathered from a wide variety of sources, and it can be found here.

Text Classification

Model: distilbert-base-uncased-finetuned-sst-2-english

Link: https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english

BERT (Bidirectional Encoder Representations from Transformers) is a groundbreaking natural language processing model introduced by Google. It revolutionized the field of NLP by employing a bidirectional transformer architecture, allowing the model to understand context from both the left and right sides of a word. Unlike previous models that processed text sequentially, BERT uses a masked language model approach during pre-training, wherein it randomly masks words and learns to predict them based on the surrounding context. This pre-training process enables BERT to capture deep contextual relationships within sentences, making it highly effective for a wide range of NLP tasks, such as sentiment analysis, named entity recognition, and text classification. However, BERT’s large size and computational demands limit its practical deployment in certain resource-constrained scenarios.

DistilBERT: Efficient Alternative to BERT

DistilBERT, on the other hand, addresses BERT’s resource-intensive limitations by distilling its knowledge into a more compact form. Introduced by Hugging Face, DistilBERT employs a knowledge distillation technique, whereby it is trained to mimic the behavior of the larger BERT model. Through this process, unnecessary redundancy in BERT’s parameters is eliminated, resulting in a significantly smaller and faster model without compromising performance. DistilBERT maintains a competitive level of accuracy compared to BERT while reducing memory usage and inference time, making it an attractive choice for applications where computational resources are a constraint. Its effectiveness in various NLP tasks has cemented its position as an efficient and practical alternative to the original BERT model. DistilBERT retains approximately 97% of BERT’s accuracy while being 40% smaller and 60% faster.

Model Details:

• Parameters: 67 million
• Transformer Layers: 6
• Embedding Layer: Included
• Classification Layer: Softmax
• Attention Heads: 12
• Vocabulary Size: 30522
• Maximum Sequence Length: 512 tokens

Choosing DistilBERT for classification tasks can offer a balance between efficiency and performance. Its faster inference, reduced resource requirements, competitive accuracy, and seamless integration make it an attractive option for a wide range of real-world applications where computational efficiency and effectiveness are key considerations.

Code Snippet:

import torch

from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained(“distilbert-base-uncased-finetuned-sst-2-english”)

model = AutoModelForSequenceClassification.from_pretrained(“distilbert-base-uncased-finetuned-sst-2-english”)

import pandas as pd

torch.cuda.set_device(0)

model.cuda()

df = pd.read_csv(dataset path)

df.head()

df.drop(df.loc[df[‘Sentiment’]==’neutral’].index, inplace=True)

X = df.iloc[:,column for sentiment evaluation]

y = df.iloc[:,target sentiment]

device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)

#metrics for the model

mydict = {‘positive’:1, ‘negative’:0, 1:1, 0:0}

count = 0

correct = 0

wrong = 0

wrong_dict = {}

for input_sequence in X:

  try:

    if y[count] == ‘neutral’:

      raise Exception(“Neutral”)

    inputs = tokenizer(input_sequence, return_tensors=”pt”).to(device)

    with torch.no_grad():

      logits = model(**inputs).logits

    predicted_class_id = logits.argmax().item()

    if predicted_class_id == mydict[y[count]]:

      correct += 1

    else:

      wrong +=1

      wrong_dict[input_sequence] = predicted_class_id

  except:

    pass

  count += 1

  print(count,’/50000 complete’, end = ‘\r’)

  # if count == 20:

  #   break

print(‘\nCorrect:’, correct)

print(‘Wrong:’, wrong)

print(len(wrong_dict))

print(‘Accuracy:’, correct/(correct+wrong))

fp = 0

fn = 0

for x in wrong_dict:

    if wrong_dict[x] == 0:

        fn += 1

    else:

        fp += 1

num_negatives = 0

num_positives = 0

for x in y:

    if x == 0:

        num_negatives += 1

    else:

        num_positives += 1

print(‘Precision:’, (num_positives-fn)/(num_positives-fn + fp))

print(‘Recall:’, (num_positives-fn)/(num_positives-fn + fn))

print(‘F1:’, (2*(num_positives-fn))/(2*(num_positives-fn) + fp + fn))

FinBERT: Specialized Financial Analysis Model

Link: https://huggingface.co/ProsusAI/finbert

FinBERT is a specialized variant of the BERT (Bidirectional Encoder Representations from Transformers) model, tailored specifically for financial text analysis. Developed by Yumo Xu and his team at RoBERTa Financial, FinBERT is pre-trained on a massive corpus of financial news articles, reports, and other domain-specific data. This pre-training process enables FinBERT to acquire a deep understanding of financial language, including intricate terminologies, domain-specific jargon, and market sentiments.

The distinguishing feature of FinBERT lies in its fine-tuning process, where it is adapted to perform specific financial NLP tasks, such as sentiment analysis, stock price prediction, and event classification. By fine-tuning on task-specific datasets, FinBERT gains the ability to extract nuanced financial insights, categorize financial events accurately, and analyze market sentiments effectively. As a result, FinBERT has proven to be a powerful tool for financial professionals, enabling them to make more informed decisions and obtain deeper insights from the vast ocean of financial text data.

FinBERT is pre-trained on a large corpus of financial text data, enabling it to learn the nuances and specific vocabulary of the financial domain. This pre-training process involves predicting missing words in sentences and is supervised using a financial sentiment dataset, which helps the model learn to classify sentiment accurately.

FinBERT Model Details

• Hidden Layers: 12
• Attention Heads: 12
• Maximum Token Input: 512
• Vocabulary Size: 30873

For more detailed information, visit: https://github.com/yya518/FinBERT

Choosing FinBERT can be a highly advantageous decision for financial text analysis due to its domain-specific expertise and fine-tuned capabilities. Unlike general-purpose NLP models, FinBERT is specifically trained on a vast corpus of financial data, granting it a profound understanding of the intricacies and nuances of financial language. This domain-specific knowledge enables FinBERT to accurately interpret financial jargon, capture sentiment nuances, and comprehend market-related events, making it an invaluable asset for tasks such as sentiment analysis, event classification, and financial news summarization.

Moreover, FinBERT’s fine-tuned nature allows it to excel in financial-specific tasks by adapting to the unique characteristics of financial datasets. Through the fine-tuning process, it learns to extract financial insights with precision, providing actionable intelligence for traders, investors, and financial analysts. By leveraging FinBERT, financial professionals can gain a competitive edge, make well-informed decisions, and navigate the complexities of the financial domain with a powerful and specialized language model at their disposal.

Code snippet:

tokenizer = AutoTokenizer.from_pretrained(“ProsusAI/finbert”)

model = AutoModelForSequenceClassification.from_pretrained(“ProsusAI/finbert”)

finbert-tone

Link: https://huggingface.co/yiyanghkust/finbert-tone

FinBERT-tone is an extension of the FinBERT model, designed to address the additional challenge of sentiment analysis in financial text. Developed by the same team at RoBERTa Financial, FinBERT-tone builds upon the foundation of FinBERT by incorporating a novel aspect – capturing the fine-grained tone of financial news articles. Unlike traditional sentiment analysis, which often focuses on binary positive/negative sentiments, FinBERT-tone aims to discern a more nuanced sentiment spectrum, encompassing positive, negative, and neutral tones.

This extension involves training FinBERT-tone on a specialized dataset that includes financial news articles annotated with granular sentiment labels. By fine-tuning on this tone-specific dataset, FinBERT-tone hones its ability to gauge the varying degrees of sentiment in financial text, offering a more comprehensive and accurate sentiment analysis solution for financial professionals. With the capability to interpret subtle sentiment fluctuations in the market, FinBERT-tone empowers users to make well-calibrated decisions and better understand the emotional aspects that influence financial events, making it a valuable tool for sentiment-aware financial analysis.

FINBERT-tone Model Details

• Fine-tuned on: 10,000 manually annotated sentences from analysis reports
• Improved Performance: Better performance on financial tone analysis tasks
• Hidden Layers: 12
• Attention Heads: 12
• Maximum Token Input: 512
• Vocabulary Size: 30873

For more detailed information, visit: https://github.com/yya518/FinBERT

This model was selected because it can prove to be a strategic advantage for financial professionals seeking sophisticated sentiment analysis capabilities. Unlike traditional sentiment analysis models, FinBERT-tone offers a more nuanced approach by capturing the fine-grained tone of financial news articles. Its specialized training on a dataset annotated with granular sentiment labels allows it to discern subtle variations in sentiment, encompassing positive, negative, and neutral tones in financial text. As a result, FinBERT-tone provides a more comprehensive understanding of the emotional undercurrents within the market, empowering users to make well-informed decisions and respond proactively to sentiment shifts.

By leveraging FinBERT-tone, financial analysts, traders, and investors can gain deeper insights into market sentiment and sentiment-driven trends. Its nuanced sentiment analysis enables users to detect shifts in investor confidence, market sentiment, and public opinion, providing a critical edge in navigating the complexities of financial markets. Additionally, the model’s fine-tuned expertise in financial language ensures accurate interpretation of domain-specific jargon and context, making it an invaluable tool for sentiment-aware financial analysis, risk management, and decision-making.

Code Snippet:

from transformers import BertTokenizer, BertForSequenceClassification

from transformers import pipeline

finbert = BertForSequenceClassification.from_pretrained(‘yiyanghkust/finbert-tone’,num_labels=3)

tokenizer = BertTokenizer.from_pretrained(‘yiyanghkust/finbert-tone’)

nlp = pipeline(“sentiment-analysis”, model=finbert, tokenizer=tokenizer, device = 0)

Continue to Part 2 Link:  Evaluating NLP Models for Text Classification and Summarization Tasks in the Financial Landscape – Part 2

Conclusion

In this first part, we’ve delved into the crucial role of high-quality datasets and explored the capabilities of foundational NLP models like distilbert-base-uncased-finetuned-sst-2-english. Understanding the significance of data and model selection sets the stage for our deep dive into specialized models tailored for financial analysis.

Stay tuned for Part 2, where we’ll explore advanced models like FinBERT and FinBERT-tone, designed to provide nuanced sentiment analysis and tone interpretation in the financial domain. These tools empower professionals to gain invaluable insights and make well-informed decisions in a rapidly evolving market landscape.

The post Evaluating NLP Models for Text Classification and Summarization Tasks in the Financial Landscape – Part 1 appeared first on Indium.

Global health data combines information from several sources to evaluate health trends, results, and the success of healthcare initiatives. It includes information about the patient’s medical records and diagnosis.

Here are some key areas to focus on in healthcare data:

• A lack of standardized interfaces and protocols constrains exchanging capabilities.
• Medical equipment utilizes exclusive data forms and protocols for communication, especially EHR from different healthcare devices.
• The level of setting initiatives varies between vendors, nations, and regions, which might result in discrepancies in medical procedures and data interchange.

Business Challenges

To ensure patients’ data portability and safety, standardization is essential and needed since the healthcare industry utilizes many electronic devices for data capture.

The lack of interoperability across various healthcare systems and devices is one of the significant challenges. Healthcare equipment and systems might interpret the data differently, which could result in discrepancies. Complex integration interfaces, challenges in connecting to different networks, and concerns with the compatibility of devices across versions are critical for the HC Domain. Data exchange between devices and healthcare providers is another challenge. Porting patients’ data across vendors is very much required for all operational changes.

Azure FHIR enables easy interoperability and integration with healthcare systems.

It allows data exchange among healthcare providers and applications, making it easier for them to make better decisions based on the reports and information.

FHIR (Fast healthcare interoperability resource)  

In Azure, the FHIR is important in exchanging electrical healthcare data in a standardized structure. Each resource will be created for each domain, for example, patient demography, patient observation, clinical documents, etc. Each resource will have a standardized format to capture the data. The FHIR search API is a powerful search mechanism to retrieve specific information about the resources based on the particular specifications we need. If the base resource does not define the data, the extension will help include custom data. Here are the key aspects of Azure FHIR:

Data Storage:  

For the storage of FHIR data, Azure FHIR offers a fully controlled, flexible, and reliable solution. Azure provides the managed services to store and access the data.
Azure uses Blob Storage and Data Lake Storage.

FHIR APIs and Services:

A group of RESTful APIs (application programming interfaces) that adhere to the FHIR protocol are made available by Azure FHIR. These APIs allow programmers to handle medical records and clinical information, interface with FHIR resources, and help retrieve, update, and delete records based on requirements.

 Scalability and performance:

Azure FHIR uses the Azure cloud platform’s scalability and performance features. Even during times of high demand, it can manage enormous quantities of medical data, maintain rapid exchange rates, and offer quick access to FHIR services.

What is FHIR BUNDLE? 

In FHIR, many more specifications are involved in structuring the data from the electronic device. For example, if the patient is under observation for an allergy or MRI scan, the data will also be in an image format with documentation. In this case, sending only the patient observations to the application devices will not be sufficient. It also requires patient information to map this. As we discussed earlier, each domain will have different resources. To solve this problem, the FHIR BUNDLE is needed. It helps exchange data from multiple sources to the application devices or the web server.

There are four types involved in the FHIR bundle,

1. 1. Transaction
2. 2. Messages
3. 3. History
4. 4. Documents

Here, the transaction type is used to create, update, and delete resources as a part of a single-unit transaction. It works on collecting requests from each resource and submitting them to the FHIR server. For each help, a separate request should be added. If any one of the requests fails while advancing to the FHIR server, the whole thing will fail. There is no partial result or success in the transaction-type FHIR bundle.

The Azure API for the FHIR resources has AAD (Azure Active Directory), which helps with authentication for the user. The FHIR resource can be written in any programming language, such as Java, Python, etc. The resources will be created in JSON or XML format, but the standard should be followed while creating the resources.

DICOM:  

DICOM (Digital Imaging and Communications in Medicine) helps convert the various types of image data, such as MRIs, CT scans, and X-rays, into meaningful data. For example, it details the parameters, patient demography, etc. It is crucial for the consistency and interoperability of the exchange of medical data and images. There will be a standard format achieved here for the observations. It will also support better treatment planning.

ImagingStudy FHIR:  

FHIR has an ImagingStudy resource type, which helps exchange the data from the image to any other application device. There will be more series elements and instances associated with this imaging study. The standard is constructed with the identifiers for the image before it is published. Then, the ImagingStudy resource type is created, and the request can be made to get the response from the server. The endpoint from the server will be the URL. This URL will help get DICOM information about the study. The “study” will have the UID (unique identifier) and modality. Each study will have a “series,” and it will have more details in it, for example, the body part where it affects. There are instances where I will be giving in-depth information.

Conclusion   

To summarize this study, FHIR facilitates interoperability by offering a standardized framework for exchanging medical data. Using bundles helps guarantee that relevant resources are gathered together, improving data management and system interaction.

The FHIR resources with bundles help exchange health care data from multiple resources, enable effective querying and searching, facilitate thorough data exchange, and improve data integrity. Data integrity is enhanced by employing transaction types in the FHIR bundle. ImageStudy resources in FHIR support interoperability, enable thorough imaging data transmission, improve care coordination, optimize data administration, provide easy connection with other healthcare resources, and encourage research and analytics. These benefits help healthcare providers offer better patient care, more effective processes, and better use of medical imaging data.

The post Achieving Health care Interoperability through Cloud-based Data Integration appeared first on Indium.

Power BI has transformed data analytics and reporting by enabling businesses to turn unstructured data into actionable insights. The capacity to produce interactive reports and share them with stakeholders is critical for making data-driven decisions. This article focuses on the publication of Power BI reports, emphasizing the use of QR codes for increased usability and robust data security measures for controlled access to reports. It connects to many data sources, transforms data, and provides aesthetically appealing reports and dashboards with user-friendly features for technical and non-technical users. Real-time data collaboration and monitoring increase the platform’s value for enterprises of all sizes. The advantages of Power BI report publication include a centralized platform for report delivery, which provides all interested parties with access to up-to-date data. By publishing to the BI service, reports can be accessed anytime and anywhere via web browsers or mobile devices.

For businesses that manage sensitive information, data security is a crucial consideration. Robust security features in Power BI allow you to control who gets access to reports, dashboards, and datasets. This protects data privacy and integrity by ensuring that only authorized persons can access sensitive information. More control over data access is possible thanks to data classification and row-level security. The article will cover how to incorporate QR codes into Power BI reports best practices for controlling report access and Power BI’s data security features. By the end, readers will fully comprehend how to implement data security measures, control report access, and publish Power BI reports using QR codes.

Leveraging QR Codes for Expanded Report Distribution in Power BI

Power BI QR Codes improve report dissemination by increasing accessibility. Users can scan and retrieve reports via QR codes, reducing the need for manual distribution. This feature allows companies to reach a broader user base, including external stakeholders and partners, improving collaboration and engagement. QR codes provide users with ease and mobility by allowing them to access reports on the move via mobile devices. Power BI enables enterprises to disseminate reports efficiently and open new opportunities in data-driven decision-making using QR codes. The best practices are detailed below,

1. 1. Clear and Concise QR Codes: Create simple QR codes to scan and comprehend. Avoid clogging the code with extraneous information, and ensure it guides users to the desired report or dashboard.
2. 2. Strategic Placement: Display QR codes where users will likely come across them, such as physical areas, conference materials, or marketing collateral. Consider including QR codes in presentations, business cards, or promotional materials to increase interaction.
3. 3. Mobile Optimization: Because QR codes are frequently scanned with smartphones or tablets, optimize reports and dashboards for mobile devices. Make sure the reports are responsive, easy to use, and give a consistent user experience on mobile platforms.

Similarly, the challenges are detailed below.

1. 1. User Familiarity: Although QR codes are becoming increasingly prevalent, some users may still be unsure how to scan them. Along with the QR code, include directions or a brief explanation to help consumers obtain the reports.
2. 2. Data Security: Ensure that the reports distributed via QR codes follow data security guidelines. To protect data integrity and privacy, limit access to critical information, and apply suitable permissions and user roles inside Power BI.

Comparison to traditional distribution methods is as follows,

1. 1. Efficiency: QR codes simplify report distribution by eliminating the need for manual sharing or emailing files. Users can save time and effort by scanning the QR code to access reports rapidly.
2. 2. Accessibility: QR codes allow consumers to obtain reports on-demand, whenever and wherever they want, using their mobile devices. This ease of access enhances the user experience and ensures that essential data is quickly available when required.
3. 3. Engagement: QR codes are a dynamic and engaging approach to communicating reports, attracting users’ interest and motivating them to dig deeper into the information offered. This can lead to enhanced stakeholder participation and collaboration.

Generating and Linking QR code to Power BI reports

The Power BI reports or dashboards that correlate with the produced QR codes must be connected to them. Through its embedding and sharing features, Power BI offers a simple method for connecting QR codes to particular objects. Users can publish reports, create shareable links, and embed these links in QR codes using the Power BI service. Guaranteeing that the proper reports or dashboards are directly linked to the QR codes is crucial. Verifying the links’ accuracy, a second time is essential to prevent confusion or improper access. Additionally, users will always have access to the most recent versions of the Power BI objects if QR codes are often updated whenever reports or dashboards are changed or replaced. It is advisable to adhere to the following best practices to make use of QR codes in Power BI report publishing as effectively as possible:

1. 1. To encourage quick access to reports, strategically place QR codes in areas that are visible and easily accessible, such as conference rooms, office spaces, or product packaging.
2. 2. Set the scene: Give QR code labels or instructions that are crystal clear and explain what they are for and what report they go to. This makes the code more meaningful and encourages users to scan it.
3. 3. Test QR codes: Before using them broadly, test them to ensure they work properly and go to the dashboards or reports we want. This reduces the possibility of outdated information or broken links.
4. 4. Track the application and efficiency of QR codes by utilizing analytics and usage metrics. This offers perceptions on user engagement, report popularity, and development opportunities.

Organizations can successfully include QR code integration in Power BI report publishing by adhering to these best practices, improving accessibility and user experience for report consumers.

Applying Data Security and Access Control for Power BI Reports

Power BI provides strong data security mechanisms such as Row-Level Security (RLS), data classification, and access control. RLS lets enterprises create row-level security restrictions, guaranteeing that users can only access data that they are permitted to see. This is especially useful for sensitive or secret data that must be restricted based on user roles or specified attributes. Organizations can use Power BI to deploy RLS by creating security roles and assigning them to individuals or groups. The Power BI model’s dynamic filtering rules allow data to be filtered based on each user’s designated role or traits, ensuring data access is confined to the proper personnel.

Data classification in Power BI allows businesses to categorize datasets and reports with varying degrees of sensitivity, giving them greater control over access and sharing capabilities. Security groups serve as containers for grouping people with comparable access limitations, making applying security roles and access controls easier. Power BI enables enterprises to design many roles, such as viewers, contributors, and administrators, each with its own access privileges and capabilities. Individual users or security groups can be allocated these roles, providing granular control over access roles and permissions.

Organizations can directly assign responsibilities and rights to specific team members using Power BI, ensuring only authorized individuals or teams can access dashboards or reports containing sensitive or restricted data. This protects data while encouraging collaboration and data-driven decision-making based on particular criteria. Organizations may implement robust security controls, protect sensitive information, and ensure that data access is allowed to the appropriate persons or groups by leveraging Power BI’s data security features. This improves data confidentiality, encourages cooperation, and allows for informed decision-making while ensuring data integrity and compliance.

Best Practices and advantages of QR report publishing over other methods.

Using QR codes in Power BI report publishing has many benefits and provides a simple and effective method for sharing and accessing reports. Creating reports with the end user in mind when disseminating them using QR codes is crucial. Improve the navigation, visualizations, and layout to provide a simple and effective user experience. Use titles, labels, and apparent directions to inform users on how to engage with the report. To ensure the reports are understandable and accessible, consider the display size and orientation of the device consumers will be scanning the QR codes.

The data models, queries, and visualizations must all be optimized for the best performance of published reports. Use caching techniques and data refresh schedules to reduce load times and increase responsiveness. Utilize query folding strategies and apply filters to reduce the volume of data retrieved, resulting in effective data retrieval and visual presentation. Numerous platforms, including websites, intranets, mobile applications, and even physical materials like posters and brochures, can disseminate and display QR codes. Utilize the best platform based on the target audience’s preferences for accessibility. Organizations can enhance report visibility and reach a larger audience by offering a variety of channels for report sharing.

Reports that use QR codes have several benefits over those that use more conventional platforms. They offer users a quick and seamless access experience, to start. Saving time and effort, scanning a QR code replaces manual searching or navigating through complex directories or URLs. Additionally, users can get reports on the move without relying on specialized devices or software by scanning QR codes displayed in public spaces. This method is affordable, corresponds with environmental objectives, and improves operational effectiveness.

Conclusion

The QR code functionality in Power BI for report publication makes it easier to access reports and improves the user experience. The incorporation of QR codes into Power BI reports increases exposure and accessibility. Row-level security, data classification, and access control are all data security measures that keep sensitive data safe. Organizations should focus on generating user-friendly reports, increasing performance, and employing diverse channels for widespread report communication to enhance Power BI utilization. Power BI is projected to give greater insights and automated data analysis as artificial intelligence and machine learning improve. Power BI will continue to focus on mobile optimization, making reports and dashboards mobile-friendly. Real-time teamwork and data-driven decision-making will be possible thanks to collaboration tools. Enhanced security features and governance controls will address data privacy and compliance issues. Power BI enables businesses to acquire relevant insights, communicate productively, and maintain data security. Its ongoing development will allow for even more impactful and secure data-driven decision-making.

The post Report Publishing in PowerBI to a Larger user base appeared first on Indium.

Pre-trained models based on Transformers have achieved exceptional performance across a spectrum of tasks within Natural Language Processing (NLP). However, these models often comprise billions of parameters, resulting in a resource-intensive and computationally demanding nature. Consequently, their suitability for devices with constrained capabilities or applications prioritizing low latency is limited. In response, model compression has emerged as a viable solution, attracting significant research attention.

This article provides a comprehensive overview of Transformer compression, centered on the widely acclaimed BERT model. Within, we delve into the most recent advancements in BERT compression techniques, offering insights into the optimal strategies for compressing expansive Transformer models. Furthermore, we aim to illuminate the mechanics and effectiveness of various compression methodologies.

Fig. Pre-training large scale models

Introduction

Tasks such as sentiment analysis, machine reading comprehension, question answering, and text summarization have benefited from pre-training large-scale models on extensive corpora, followed by fine-tuning for specific tasks. While earlier methods like ULMFiT and ELMo utilized recurrent neural networks (RNNs), more recent approaches leverage the Transformer architecture, which heavily employs the attention mechanism.

Prominent pre-trained Transformers like BERT, GPT-2, XLNet, Megatron-LM, Turing-NLG, T5, and GPT-3 have significantly advanced NLP. However, their size poses challenges, consuming substantial memory, computation, and energy. This becomes more pronounced when targeting devices with lower capacity, such as smartphones or applications necessitating rapid responses, like interactive chatbots.

To contextualize, training GPT-3, a potent and sizable Transformer model, on 300 billion tokens costs well over 12 million USD. Moreover, utilizing such models for fine-tuning or inference demands high-performance GPU or multi-core CPU clusters, incurring significant monetary expenses. Model compression offers a potential remedy.

Breakdown of BERT

Bidirectional Encoder Representations from Transformers, commonly known as BERT, constitutes a Transformer-based model that undergoes pre-training using extensive datasets sourced from Wikipedia and the Bookcorpus dataset. This pre-training involves two key objectives:

• Masked Language Model (MLM): These objectives aid BERT in grasping sentence context by learning to predict masked-out words within the text.

• Next Sentence Prediction (NSP): BERT also learns relationships between two sentences through NSP, which predicts whether one sentence follows the other in a given text.

Subsequent iterations of Transformer architectures have refined these training objectives, resulting in enhanced training techniques.

Fig. BERT model

The processing flow of the BERT model divides input sentences into WordPiece tokens, a type of tokenization that strengthens input vocabulary representation while condensing its size. Subworlds are used to break apart complex words to do this. Notably, these subworlds can create new words not in the training set, strengthening the model’s resistance to terms that aren’t in its lexicon. BERT is characterized by a classification token ([CLS]) before input tokens. The output corresponding to this token is used for tasks aiming at the whole input. Sentence pairs involved in situations are concatenated with a separator character ([SEP]) between them.

Each WordPiece token in BERT is encoded using three vectors: ticket, segment, and position embeddings. These embeddings are summed and fed through the model’s core, the Transformer backbone. This results in output representations directed into the final layer, tailored to the specific application (for instance, a sentiment analysis classifier).

The Transformer backbone comprises stacked encoder units, each featuring two primary sub-units: a self-attention sub-unit and a feed-forward network (FFN) sub-unit. Both sub-units possess residual connections for enhanced learning. The self-attention sub-unit incorporates a multi-head self-attention layer alongside a fully connected layer before and after. Meanwhile, the FFN sub-unit exclusively employs thoroughly combined layers. Three hyper-parameters define BERT’s architecture:

• The number of encoder units (L),
• The embedding vector (H) size and the number of attention heads in each self-attention layer (A).
• L and H determine the model’s depth and width, respectively, while A, an internal hyper-parameter, influences the contextual relations each encoder focuses on.

Compression Methods

Various compression methods address BERT’s complexity. Quantization reduces unique values for weights and activations, lowering memory usage and potentially enhancing inference speed. Pruning encompasses unstructured and structured approaches, removing redundant weights or architectural components. Knowledge Distillation trains smaller models using larger pre-trained models’ outputs. Other techniques like Matrix Decomposition, Dynamic Inference Acceleration, Parameter Sharing, Embedding Matrix Compression, and Weight Squeezing contribute to compression efforts.

1. Quantization

Quantization involves the reduction of unique values necessary to depict model weights and activations. This reduction enables their representation using fewer bits, leading to diminished memory usage and reduced precision in numerical computations. Quantization might enhance runtime memory consumption and inference speed, especially when the foundational computational hardware is engineered to handle lower-precision numerical values. An example is the utilization of tensor cores in recent Nvidia GPU generations. Programmable hardware like FPGAs can also be meticulously tailored to optimize bandwidth representation. Furthermore, quantization to intermediate outputs and activations can expedite model execution further.

2. Pruning

Pruning methodologies for BERT predominantly fall within two distinct categories:

(i). Unstructured Pruning: Also referred to as sparse pruning, unstructured pruning involves removing individual weights identified as least crucial within the model. The significance of these weights can be assessed based on their absolute values, gradients, or customized measurement metrics. Given BERT’s extensive employment of fully connected layers, unstructured pruning holds potential efficacy. Examples of unstructured pruning methods encompass magnitude weight pruning, which discards weights close to zero; movement-based pruning, which eliminates weights tending towards zero during fine-tuning; and reweighted proximal pruning (RPP), which employs iteratively reweighted ℓ1 minimization followed by the proximal algorithm to separate pruning and error back-propagation. Due to its weight-by-weight approach, unstructured pruning can result in arbitrary and irregular sets of pruned weights, potentially reducing the model size without significantly improving runtime memory or speed unless applied on specialized hardware or utilizing specialized processing libraries.

(ii). Structured Pruning: Structured pruning targets the elimination of structured clusters of weights or even entire architectural components within the BERT model. This approach simplifies and reduces specific numerical modules, leading to enhanced efficiency. The focal areas of structured pruning comprise Attention Head Pruning, Encoder Unit Pruning, and Embedding Size Pruning.

3. Knowledge Distillation

Knowledge Distillation involves training a compact model (referred to as the student) by utilizing outputs generated by one or more extensive pre-trained models (referred to as the teachers) through various intermediate functional components. This exchange of information might occasionally pass through an intermediary model. Within the context of the BERT model, numerous intermediate outcomes serve as potential learning sources for the student. These include the logits within the concluding layer, the outcomes of encoder units, and the attention maps.

4. Other methods

1. Matrix Decomposition

2. Dynamic Inference Acceleration

3. Parameter Sharing

4. Embedding Matrix Compression

5. Weight Squeezing

Effectiveness of Compression Methods

Quantization and unstructured pruning offer the potential to decrease the model size. Yet, their impact on runtime inference speed and memory consumption remains limited, unless applied on specialized hardware or using specialized processing libraries. Conversely, when deployed on suitable hardware, these techniques can significantly enhance speed while maintaining performance levels with minimal compromise. Therefore, it’s crucial to consider the target hardware device before opting for such compression methods in practical scenarios.

Knowledge distillation has demonstrated strong compatibility with various student models, and its unique approach sets it apart from other methods, making it a valuable addition to any compression strategy. Specifically, distilling knowledge from self-attention layers, if feasible, holds integral importance in Transformer compression.

Alternatives like BiLSTMs and CNNs boast an additional advantage in terms of execution speed compared to Transformers. Consequently, replacing Transformers with alternative architectures is a more favorable choice when dealing with stringent latency requirements. Additionally, dynamic inference techniques can expedite model execution, as these methods can be seamlessly integrated into student models sharing a foundational structure akin to Transformers.

A pivotal insight from our preceding discussion underscores the significance of amalgamating diverse compression methodologies to realize truly effective models tailored for edge environments.

Applications of BERT

BERT’s capabilities are extensive and versatile, enabling the development of intelligent and efficient search engines. Through BERT-driven studies, Google has advanced its ability to comprehend the intent behind search queries, delivering relevant results with increased accuracy.

Text summarization represents another area where BERT’s potential shines. BERT can be harnessed to facilitate textual content summarization, endorsing a well-regarded framework that encompasses both extractive and abstractive summarization models. In the context of extractive summarization, BERT identifies the most significant sentences within a document, forming a summary. This involves a neural encoder creating sentence representations, followed by a classifier that predicts which sentences merit inclusion as part of the summary.

The advent of SCIBERT underscores the significance of BERT in medical literature. Given the exponential growth in clinical resources, NLP powered by SCIBERT has become a vital tool for large-scale data extraction and system learning from these documents.

BERT’s contribution extends to the realm of chatbots as well. It played a pivotal role in enhancing the Stanford Question Answering Dataset (SQuAD), which involves reading comprehension tasks based on questions posed to Wikipedia articles. Leveraging BERT’s functionality, chatbot capabilities can be extended from handling small to substantial text inputs.

Moreover, BERT’s utility encompasses sentiment analysis, which involves discerning sentiments and emotions conveyed in textual content. Additionally, BERT excels in tasks related to text matching and retrieval, where it aids in identifying and retrieving relevant textual information.

The post Text Analytics with low latency and high accuracy: BERT – Model Compression appeared first on Indium.

According to Cybersecurity Ventures, it is estimated that the global annual cost of cybercrime is predicted to reach $8 trillion USD in 2023. Additionally, the damage costs from cybercrime are anticipated to soar to $10.5 trillion by 2025. The magnitude of these attacks emphasizes the critical need for organizations to prioritize cybersecurity measures and remain vigilant against potential threats.

While cyber threats continue to evolve, technology consistently showcases its capability to outsmart them. Advanced AI systems proactively detect threats, and quantum cryptography introduces near-unbreakable encryption. Behavioral analytics tools, like Darktrace, pinpoint irregularities in network traffic, while honeypots serve as decoys to lure and study attackers. A vigilant researcher’s swift halting of the WannaCry ransomware’s spread exemplifies technology’s edge. These instances collectively underscore technology’s potential for countering sophisticated cyber threats.

Generative AI (GenAI) is revolutionizing cybersecurity with its advanced machine learning algorithms. GenAI identifies anomalies that often signal potential threats by continuously analyzing network traffic patterns. This early detection allows organizations to respond swiftly, minimizing potential damage. GenAI’s proactive and adaptive approach is becoming indispensable as cyber threats grow in sophistication, with its market valuation projected to reach USD 11.2 billion, with a CAGR of 22.1% by 2032, reflecting its rising significance in digital defense strategies.

Decoding the GenAI mechanism

The rapid evolution of Generative AI, especially with the advent of Generative Adversarial Networks (GANs), highlights the transformative power of technology. Companies, including NVIDIA, have successfully leveraged GenAI for security, using it to detect anomalies and enhance cybersecurity measures. Since its inception in the 1960s, GenAI has transitioned from basic data mimicry to creating intricate, realistic outputs. Presently, an impressive 81% of companies utilize GenAI for security. Its applications span diverse sectors, offering solutions that were once considered the realm of science fiction. NVIDIA’s success story is a testament to the relentless pursuit of innovation and the boundless possibilities of AI.

GenAI performs data aggregation to identify security threats and take the necessary actions to maintain data compliance across your organization. It collects data from diverse sources, using algorithms to spot security anomalies. Upon detection, it alerts administrators, isolates affected systems or blocks malicious entities. Ensuring data compliance, GenAI encrypts sensitive information, manages access, and conducts audits. According to projections, by 2025, GenAI will synthetically generate 10% of all test data for consumers dealing with use cases. Concurrently Generative AI systems like ChatGPT and DALL-E 2 are making waves globally. ChatGPT acts as a virtual tutor in Africa and bolsters e-commerce in Asia, while DALL-E 2 reshapes art in South America and redefines fashion in Australia. These AI systems are reshaping industries, influencing how we learn, create and conduct business.

Generative AI, through continuous monitoring and data synthesis, provides real-time security alerts, ensuring swift threat detection and response. This AI capability consolidates diverse data into a centralized dashboard, offering decision-makers a comprehensive view of operations. Analyzing patterns offers insights into workflow efficiencies and potential bottlenecks, enhancing operational visibility. In 2022, around 74% of Asia-Pacific respondents perceived security breaches as significant threats. With Generative AI’s predictive analysis and trend identification, businesses can anticipate challenges, optimize operations, and bolster security.

Tomer Weingarten, the co-founder and CEO of SentinelOne, a leading cybersecurity company, said, “Generative AI can help tackle the biggest problem in cybersecurity now:” With GenAI, complex cybersecurity solutions can be simplified to yield positive outcomes.

The role of Generative AI in cybersecurity risk mitigation

Reuben Maher, the Chief Operating Officer of Skybrid Solutions, who oversees strategic initiatives and has a deep understanding of the intricacies of modern enterprise challenges, stated, “The convergence of open-source code and robust generative AI capabilities has powerful potential in the enterprise cybersecurity domain to provide organizations with strong and increasingly intelligent defenses against evolving threats.”

There are many open source (Llama2, MPT, Falcon etc.)  and paid (ChatGPT, PALM, Claude etc.) which can be used based on the available infrastructure and complexity of the problem.

Fine-tuning is a technique in which pre-trained models are customized to perform specific tasks involving taking an existing model that has already been trained and adapting it to a narrower subject or a more focused goal.

It involves three key steps:

1. Dataset Preparation: Gather a dataset specifically curated for the desired task or domain.

2. Training the Model: Using the curated dataset, the pre-trained model is further trained on the task-specific data. The model’s parameters are adjusted to adapt it to the new domain, enabling it to generate more accurate and contextually relevant responses.

3. Evaluation and Iteration: Once the fine-tuning process is complete, the model is evaluated using a validation set to ensure it meets the desired performance criteria. If necessary, the process can be iterated by the model again with adjusted parameters to improve performance further.

Use case: using Generative AI models trained on open-source datasets of known cyber threats, organizations can simulate various attack scenarios on their systems. This “red teaming” approach aids in identifying vulnerabilities before actual attackers exploit them.

Proactive defense with Generative AI

Generative AI revolutionizes cybersecurity by enabling proactive defense strategies in the face of a rapidly evolving threat landscape. Through the application of Generative AI, organizations can bolster their security posture in multiple ways. First and foremost, Generative AI facilitates robust threat modeling, allowing organizations to identify vulnerabilities and potential attack vectors within their systems and networks. Furthermore, it empowers the simulation of complex cyber-attack scenarios, enabling security teams to understand how adversaries might exploit these vulnerabilities. In addition, Generative AI’s continuous analysis of network behaviors detects anomalies and deviations from established patterns, providing real-time threat detection and response capabilities. Perhaps most crucially, it excels in predicting potential cyber threats by leveraging its ability to recognize emerging patterns and trends, allowing organizations to proactively mitigate risks before they materialize. In essence, Generative AI serves as a unified and transformative solution that empowers organizations to anticipate, simulate, analyze, and predict cyber threats, ushering in a new era of proactive cybersecurity defense.

Enhanced anomaly detection:

Generative AI is renowned for recognizing patterns. Analyzing historical data through autoencoders to understand the intricate patterns establishes a baseline of a system’s “normal” behavior. When it detects deviations, such as unexpected data spikes during off-hours, it flags them as anomalies. This deep learning-driven approach surpasses conventional methods, enabling Generative AI to identify subtle threats that might elude traditional systems, making it an invaluable asset in cybersecurity.

Enhanced training in data generation

Generative AI can excel at producing synthetic data, especially images, by discerning patterns within the datasets. This unique ability enriches training sets for machine learning models, ensuring diversity and realism. It aids in data augmentation and ensures privacy by creating non-identifiable images. Whether tabular data, time series, or even intricate formats such as images and videos, Generative AI guarantees that the training data is comprehensive and mirrors real-world scenarios.

Simulating cyberattack scenarios:

In the realm of cybersecurity, the utility of Generative AI in accurately replicating training data is indeed paramount when simulating cyberattacks scenarios. This unique capability enables organizations to adopt a proactive stance by recognizing and mitigating potential threats before they escalate. Let’s delve deeper into the technical aspects, particularly addressing the challenge of dealing with highly imbalanced datasets:

Accurate Data Replication and Simulation:

Generative AI models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), excel at replicating training data accurately. Here’s how they can be applied in a cybersecurity context:

1. GANs for Data Generation: GANs consist of a generator and a discriminator. The generator learns to generate data samples that are indistinguishable from real data, while the discriminator tries to tell real data from generated data. In cybersecurity, GANs can be trained on historical data to accurately replicate various network behaviors, traffic patterns, and system activities.

2. Variational Autoencoders (VAEs): VAEs are probabilistic generative models that learn the underlying structure of data. They can be used to generate synthetic data points that closely resemble the training data while capturing its distribution. VAEs can be particularly useful for simulating rare but critical events that may occur during cyberattacks.

3. Large Language Models (LLMs): LLMs, such as GPT-4, can be harnessed for text-based data generation and enrichment. They excel in generating natural language descriptions of cybersecurity events, threat scenarios, and incident reports. This text data can augment the output of GANs and VAEs, providing additional context and narrative to the simulated data, making it more realistic and informative.

Handling Imbalanced Datasets:

Cybersecurity datasets are often highly imbalanced, with a vast majority of data points representing normal behavior and only a small fraction indicating cyber threats. Generative AI can help mitigate this issue:1.

1. Oversampling Minority Class: Generative AI can generate synthetic examples of the minority class (cyber threats) to balance the dataset. This ensures that the model is not biased towards the majority class (normal behavior).

2. Anomaly Generation: Generative AI can be fine-tuned to generate data points that resemble anomalies or rare events. This helps in simulating cyber threats effectively, even when they are infrequent in the training data.

Innovative security tool development

Generative AI can be used to devise new security tools. From generating phishing emails to counterfeit websites, harnessing this technology empowers security analysts in threat simulation, training enhancement, proactive defense and more to proactively identify potential threats and stay proactive toward ever-changing cyber challenges. However, while its potential is vast, ethical concerns arise as malevolent actors could misuse Generative AI for malicious intent. It’s imperative to establish stringent guidelines and controls to prevent such misuse.

Automated incident response and remediation:

Generative AI-driven systems offer the potential for rapid response and enhanced protection in cybersecurity by leveraging advanced algorithms to analyze and respond to threats efficiently. Here, we’ll dive into more technical details while addressing the associated challenges:

Swift Attack Analysis and Response:

Generative AI-driven systems utilize advanced machine learning and deep learning algorithms for swift attack analysis. When a potential threat is detected, these systems employ techniques such as:

1. 1. Behavioral Analysis: Continuously monitoring and analyzing network and system behavior patterns to detect anomalies or suspicious activities indicative of an attack.

2. 2. Pattern Recognition: Leveraging pattern recognition algorithms to identify known attack signatures or deviations from normal behavior.

3. 3. Predictive Analytics: Employing predictive models to forecast potential threats based on historical data and real-time information.

4. 4. Threat Intelligence Integration: Integrating real-time threat intelligence feeds to stay updated on the latest attack vectors and tactics used by malicious actors.

Challenges and Technical Details:

1. 1. False Positives:

– Addressing false positives involves refining the machine learning models through techniques like feature engineering, hyperparameter tuning, and adjusting the decision thresholds.

– Employing ensemble methods or anomaly detection algorithms can help reduce false alarms and improve the accuracy of threat detection.

2. 2. Adversarial Attacks:

– To mitigate adversarial attacks, Generative AI models can be hardened by implementing techniques such as adversarial training and robust model architectures.

– Regularly retraining models with updated data and re-evaluating their security can help in detecting and countering adversarial attempts.

3. 3. Complexity:

– To make AI models more interpretable, techniques such as model explainability and feature importance analysis can be applied. This helps in understanding why a particular decision or classification was made.

– Utilizing simpler model architectures or incorporating rule-based systems alongside AI can provide transparency in decision-making.

4. 4. Over-Reliance:

– Human experts should always maintain an active role in cybersecurity. AI-driven systems should be viewed as aids rather than replacements for human judgment.

– Continuous training and collaboration between AI systems and human experts can help strike a balance between automation and human oversight.

By effectively addressing these challenges and leveraging the technical capabilities of Generative AI, cybersecurity systems can rapidly identify, understand, and respond to cyber threats while maintaining a balance between automation and human expertise.

Navigating GenAI: Meeting complex challenges with precision

Generative AI presents a transformative world, but it is not without obstacles. Success lies in the meticulous handling of the complex challenges that arise. Explore the crucial hurdles that must be addressed responsibly and effectively to realize the potential of GenAI.

1.Data management

LLM’s pioneers of AI Evolution: Large Language Models (LLMs) are crucial for AI advancements, significantly enhancing the capabilities of artificial intelligence and paving the way for more sophisticated applications and solutions.

Third-party risks: The storage and utilization of this data by third-party AI providers can expose your organization to unauthorized access, data loss, and compliance issues. Establishing proper controls and comprehensively grasping the data processor and data controller dynamics is crucial to mitigating the risks.

2. Amplified threat landscape

Sophisticated phishing: The emergence of sophisticated phishing techniques has lowered the threshold for cybercriminals. These include deep fake videos or audio, customized chat lures, and highly realistic email duplications, which are on the rise.

Examples include CEO fraud, tax scams, COVID-19 vaccine lures, package delivery notifications, and bank verification messages designed to deceive and exploit users.

Insider threats: By exploiting GenAI, insiders with in-depth knowledge of their organization can effortlessly create deceptive and fraudulent content. The potential consequences of an insider threat involve the loss of confidential information, data manipulation, erosion of trust, and legal and regulatory repercussions. To counteract these evolving threats, organizations must adopt a multi-faceted cybersecurity approach, emphasizing continuous monitoring, employee training, and the integration of advanced threat detection tools.

3. Regulatory and legal hurdles

Dynamic compliance needs: In the ever-evolving GenAI landscape, developers and legal/compliance officers must continually adapt to the latest regulations and compliance studies. Staying abreast of new regulations and stricter enforcement of existing laws is crucial to ensuring compliance.

Exposure to legal risks: Inadequate data security measures can result in the disclosure of valuable trade secrets, proprietary information, and customer data, which can have severe legal consequences and negatively impact a company’s reputation.

For instance, recently, the European Union’s GDPR updates emphasized stricter consent requirements for AI-driven data processing, impacting GenAI developers and compelling legal teams to revisit compliance strategies.

Organizations should prioritize continuous training, engage regulatory consultants, leverage compliance software, stay updated with industry best practices, and foster open communication between legal and tech teams to combat this.

4. Opaque model

Black-box dilemma: Generative AI models, especially deep learning ones, are often opaque. It is difficult for cybersecurity experts and business leaders to trust and validate their outputs because of their high accuracy and lack of transparency in decision-making. To enhance trust and transparency, organizations can adopt Explainable AI (XAI) techniques, which aim to make the decision-making processes of AI models more interpretable and understandable.

Regulatory and compliance challenges: In sectors like finance and healthcare, where explainability is paramount, AI’s inability to justify its decisions can pose regulatory issues. Providing clear reasons for AI-driven decisions, such as loan denials or medical claim rejections. To address this, organizations can implement auditing and validation frameworks that rigorously test and validate AI decisions against predefined criteria, ensuring consistency and accountability.

Undetected biases: The inherent opaqueness of these models can conceal biases in data or decision-making. These biases might remain hidden without transparency, leading to potentially unfair or discriminatory results. In response, it’s essential to implement rigorous testing and validation processes, utilizing tools and methodologies specifically designed to uncover and rectify hidden biases in AI systems.

Troubleshooting difficulties: The lack of clarity in generative AI models complicates troubleshooting. Pinpointing the cause of errors becomes a formidable task, risking extended downtimes and potential financial and reputational repercussions. To mitigate these challenges, adopting a layered diagnostic approach combined with continuous monitoring and feedback mechanisms can enhance error detection and resolution in complex AI systems.

5. Technological adaptation

Rapid tool emergence: The unexpected rise of advanced GenAI tools like ChatGPT, Bard, and GitHub Copilot has caught enterprise IT leaders off guard. To tackle the challenges posed by these tools, implementing Generative AI Protection solutions is absolutely essential. To effectively integrate these solutions, organizations should prioritize continuous training for IT teams, fostering collaboration between AI experts and IT personnel, and regularly updating security protocols in line with the latest GenAI advancements.

Enterprises can rely on Symantec DLP Cloud and Adaptive Protection to safeguard their operations against potential attacks. These innovative solutions offer comprehensive capabilities to discover, monitor, control, and prioritize incidents. To harness the full potential of these solutions, enterprises should integrate them into their existing IT structure, conduct regular system audits, and ensure that staff are trained on the latest security best practices and tool functionalities.

Discover how Indium Software can empower organizations with Generative AI

Indium Software empowers organizations to seamlessly integrate AI-driven systems into their workplace environments, addressing comprehensive security concerns. By harnessing the prowess of GenAI, the experts at Indium Software deliver diverse solutions that elevate and streamline business workflows, leading to tangible and long-term gains.

In addition to these, the AI experts at Indium Software offer a wide range of services. These include GenAI strategy consulting, end-to-end LLm/GenAI product development, GenAI model pre-training, model fine-tuning, prompt engineering, and more.

Conclusion

In the cybersecurity landscape, Generative AI emerges as a game-changer, offering robust defenses against sophisticated threats. As cyber challenges amplify, Indium Software’s pioneering approach in harnessing GenAI’s capabilities showcases the future of digital protection. For businesses, embracing such innovations is no longer optional—survival and growth must stay ahead in this competitive digital era and safeguard their valuable assets.

The post Generative AI: A new frontier in cybersecurity risk mitigation for businesses appeared first on Indium.

MLOps evolved in response to the growing need for companies to implement machine learning and artificial intelligence models to streamline their workflows and generate better revenue from their business operations. Today, MLOps has become a household name among top business owners. The MLOps market is expected to reach a valuation of USD 5.9 billion by 2027, up from about USD 1.1 billion in 2022.

Two of the most important aspects of MLOps include model monitoring and governance. Model monitoring and governance can be used to introduce automated processes for monitoring, validating, and tracking machine learning models in production environments. It is mainly implemented to adhere to safety and security measures, follow the necessary rules and regulations, and ensure compliance with ethical and legal standards.

This blog delves into the complexities associated with model monitoring and governance implementation while underscoring the pivotal role of integrating model governance within a comprehensive framework. Dive deeper to gain insights into its potential future developments and explore how Indium Software can provide exceptional support for establishing a robust system.

The Impact of MLOps on Governance and Monitoring Practices   

Organizations need to assess the relevance of MLOps in their operations to ascertain the necessity of MLOps governance and monitoring. When the benefits outweigh the drawbacks, businesses will be motivated to diligently and systematically establish MLOps governance and monitoring protocols without exception.

Let’s examine the advantages of MLOps to understand their implications for monitoring and governance.

Streamlined ML lifecycle:  Inheriting tools such as MLflow, TensorBoard, DataRobot, and other practices ensure an efficient and optimized ML lifecycle. Setting up a streamlined ML lifecycle allows for a seamless and automated transition between each stage of the machine learning journey, from data handling to model rollout.

Continuous integration and delivery (CI/CD): The extended principle of DevOps assists organizations with automated testing, validation, and seamless deployment of models.  This availability for MLOps ensures that the ML systems remain reliable and up-to-date throughout their lifecycle, enhancing overall efficiency and reliability.

Accelerated time-to-market: By using the concept of CI/CD in MLOps, a faster and more reliable methodology is achieved, where the dependencies on human resources are minimized. Enhancingtly, enhancing the speed and reliability of getting machine learning models into production ultimately benefits the organization’s agility and ability to respond quickly to changing business needs. The whitepaper offers an in-depth expert analysis, providing a comprehensive grasp of MLOps within Time to Market (TTM).

Scalability: Given the complexity of machine learning operations, MLOps practices facilitate an easy and relaxed approach for organizations handling complex and large data sets. Practices such as automation, version control, and streamlined workflows assist in efficiently managing and expanding ML workloads, ensuring that the infrastructure and processes can adapt to growing demands without overwhelming the team.

Diverse obstacles in MLOps monitoring and governance.

MLOps seeks to refine and automate the entire ML lifecycle, transforming how organizations handle ML models. However, this brings distinct challenges to monitoring and governance. While monitoring emphasizes consistently assessing model performance and resource use, governance ensures models meet compliance, ethical standards, and organizational goals. Navigating the below challenges is essential for tapping into ML’s potential to maintain transparency, fairness, and efficiency.

Model drift detection: An underlying change in the statistical properties of the data, such as a change in trend, behavioral patterns, or other extra influences, can lead to a decline in model performance and efficiency. Detecting the drift through rigorous monitoring against actual outcomes and using statistical tests to identify significant deviations often requires model retraining or recalibration to align with the new data distribution. This unforeseen model drift persists as a challenge for MLOps monitoring and governance.

Consider the scenario where a leading fintech company deploys an ML model to regulate loan defaults. With exemplary performances during the initial stages, the model began to fall short in its performance as the economic downturn impacted the financial behaviors of borrowers. Functioning based on real-world input data, the model drifts. If robust MLOP (Machine Learning Operations) monitoring were implemented, it could detect model drift, resulting in actions such as canceling loans for defaults, evaluating proficient borrowers, enhancing credit score management, and streamlining other procedures. The imperative to model monitoring for drift is highly commendable to prevent any financial loss or to land in any monetary fraudulent situation.

Performance metrics monitoring: The significance of selecting the right metrics, setting dynamic thresholds, balancing trade-offs, and ensuring ethical considerations and regulatory compliance, unlike traditional software models, is major in performance metric checking. This intricacy goes beyond just quantifying model behavior and thereby involves continuous monitoring, interpreting metrics in context, and effectively communicating their implications to stakeholders, making it a multifaceted challenge in ML governance.

Interpretability and transparency: A readable and predictable model is pivotal for organizations’ decisions. Advanced models, such as deep neural networks,  popularly termed black boxes,  appear complex and decipherable. Without transparency, detecting biases, ensuring regulatory compliance, building trust, and establishing feedback mechanisms become problematic. A few methods or techniques, such as Partial Dependence Plots (PDP), Local Interpretable Model-agnostic Explanations (LIME), Shapely Addictive explanations (SHAP), and Rule-Based Models are suggestions that can be employed to enhance interpretability  This hardship exhibits governance challenges that must be outsmarted by balancing high-performance modeling with interpretability in the MLOps landscape.

Audit traits: Establishing a systematic record of events throughout the lifecycle of an ML  model is essential for ensuring transparency and accountability. Given the immense volume of data growth, the demand for secure, tamper-proof, and real-time logging and integration across various tools such as MLflow, TensorBoard, Amazon SageMaker model monitor, Data version control, Apache Kafka, and many more is becoming increasingly imperative. This underscores a significant challenge in terms of governance and monitoring. Therefore, a robust and comprehensive approach to model monitoring and governance guarantees.

• Transparency and accountability throughout the ML model’s lifecycle
• Integration across various tools.
• Security and compliance of logs with relevant regulations.
• Interpretable logs.

Model versioning & rollback: Tracking different iterations of machine learning models with a rollback to a previous model version is coupled with their dependencies on specific data, libraries, and configurations. This dynamic nature of ML models makes it subjective to maintain clear rollback logs for compliance, coordinate rollbacks across teams, and manage user impact, delivering serious challenges for governance and monitoring.

Below are some of the practical approaches to model versioning that can be implemented to combat the challenges of model monitoring and governance.

Version control systems: Leveraging traditional methods such as Git assists in tracking the changes in the model code, data preprocessing scripts, and configuration files by accessing the history of model development and allowing you to roll back to previous states.

Containerization: Utilizing platforms like Docker, where the entire model is locked in a container along with its dependencies and configurations, ensures that the model’s environment is consistent across different stages of development and production.

Model Versioning Tools: Using tools or platforms such as MLflow or DVC, dedicatedly designed for tracking machine learning models, their dependencies, and data lineage, where they offer features for model versioning and rollback.

Model Deployment Environments: Isolating every stage of the model environment, such as development, testing, and production, helps check for updates that are thoroughly tested before being deployed.

Artifact Repositories: Establish artifact repositories like AWS S3, Azure Blob Storage, or a dedicated model registry to store model artifacts, such as trained model weights, serialized models, and associated metadata. This makes it easy to retrieve and deploy specific model versions.

Resource utilization: Managing computational resources used by ML models throughout their lifecycle is crucial, especially given scalability demands, specific hardware needs, and cost considerations in cloud settings. While resource utilization is key to operational efficiency and cost control, governance, and monitoring face challenges in maintaining budgets, optimizing performance, and offering transparent resource usage reports.

Measures to tackle the challenges in model monitoring and governance

Ensuring robust monitoring and governance systems is paramount for companies aiming for peak productivity in MLOps. Existing rules and regulations mandate specific standards and practices that companies must adhere to in their MLOps monitoring and governance efforts, including the

• General Data Protection Regulation (GDPR): GDPR sets aside rules for carefully handling personal data.
• California Consumer Privacy Act (CCPA): ML companies in California accessing personal data must adhere to the CCPA.
• Fair Credit Reporting Act (FCRA): FCRA regulates the use of consumer credit information for risk assessment.
• Algorithmic Accountability Act: This Act assesses the accountability of machine learning and AI systems.

However, even with the regulations and legal aspects in place, ML systems may be exposed to various risks. There is always a chance of ML systems being exposed to security threats and data breaches. A company may also have to deal with legal consequences if any machine learning models fail to comply with the legal requirements. This can ultimately lead to huge financial losses for businesses.

Implementing model monitoring and governance: Why is it necessary?

With multiple benefits circulating from implementing model monitoring and governance, let’s infer the primary significance of organizations having a head-start on capitalizing on model monitoring and governance.

• Eliminate the risk of financial losses, reputational damage, and other legal consequences.
• Better visibility into their ML systems. The chances of model biases can be significantly reduced.
• Monitor their ML systems for better performance. There is also a reduced chance of machine disruption and data accuracy.
• Identify instances where models are underutilized or overutilized. This allows for better management of resources.   

Key considerations for building a monitoring and governance framework

The implementation process for an MLOps monitoring and governance framework involves the following steps:   

Pick the right framework that suits the business’s needs   

It is important to pick a monitoring and governance model that aligns with the company’s goals. Companies mostly need ML governance models for risk mitigation, compliance with regulations, traceability, and accountability. Different monitoring and governance models are available, including centralized models, decentralized models, hybrid models, etc., and the choice of machine learning model will depend on the size and complexity of the business, the industry in which the business operates, etc.   

Implement the monitoring and governance framework in the business infrastructure  

With multiple ways to implement a governance model, the perfect process depends on the existing infrastructure. Injecting an SDK (Software Development Kit) into the machine learning code is one way of implementing MLOps governance. An SDK offers interfaces and libraries for implementing various machine-learning tasks. It also helps with bias, drift, performance, and anomaly detection. These days, SDKs can also be used as version control mechanisms for ML systems.

Make the governance model comply with industrial standards  

Once the implementation phase is complete, it is time to make the MLOps model comply with the relevant regulations. Failing to comply with regulations can lead to legal consequences, including fines, penalties, and legal actions. So, organizations must consider the present regulations for MLOps business models and ensure that their ML models comply with the regulatory standards.

Integrating MLOps with DevOps

Here’s what the future of model monitoring and governance looks like:

In the future, the main focus of model monitoring and governance will lie in risk management and compliance with regulatory and ethical standards. However, we are also witnessing a shift in trend towards social responsibility. Within the next five years, companies will start implementing model monitoring and governance as a part of their obligation to society. With time, MLOps tools and frameworks will also become more sophisticated. These tools will help avoid costly AI errors and huge financial losses.   

Indium Software: The ultimate destination for diverse MLOps needs

Indium Software specializes in assisting businesses in automating ML lifecycles in production to maximize the return on their MLOps investment. We also support the implementation of model monitoring and governance in various office settings by leveraging the power of well-known ML frameworks. With over seven years of experience creating ML models and implementing model monitoring and governance solutions, our team brings exceptional technical knowledge and expertise.

Through our tested solutions, businesses can improve performance and streamline their procedures. Additionally, our services have been shown to reduce time to market by up to 40% and enhance model performance by 30%. Furthermore, we can help businesses reduce the cost of ML operations by up to 20%.   

Conclusion:

In this way, the emergence of MLOps implementation can allow businesses to make the most of ML systems. However, simply implementing MLOps is not enough. Implementing model monitoring and governance frameworks to ensure ML systems’ reliability, accountability, and ethical use is equally important.

The post Importance of Model Monitoring and Governance in MLOps appeared first on Indium.

THE RISE OF GENERATIVE AI

Generative AI has emerged as a groundbreaking field, enabling machines to create new content that mimics human creativity. This technology has been applied in various domains, including natural language processing, computer vision, and music composition. By training AI models on vast amounts of text data, they can learn patterns, generate coherent sentences, and even produce original pieces of writing. However, as the field progresses, it confronts a roadblock: the scarcity of quality training data.

THE WARNING FROM UC BERKELEY

Recently, a UC Berkeley professor raised concerns about generative AI tools “running out of text” to train on. The explosion of AI applications has consumed an enormous amount of text, leaving fewer untapped resources for training future models. The professor cautioned that if this trend continues, AI systems may reach a point where they struggle to generate high-quality outputs or, worse, produce biased and misleading content.

IMPLICATIONS FOR GENERATIVE AI

The shortage of training text could have significant consequences for the development of generative AI. First and foremost, it may limit the potential for further advancements in natural language processing. Generative models heavily rely on the availability of diverse and contextually rich text, which fuels their ability to understand and generate human-like content. Without a steady supply of quality training data, AI systems may face challenges in maintaining accuracy and coherence.

Moreover, the shortage of text data could perpetuate existing biases within AI models. Bias is an ongoing concern in AI development, as models trained on biased or incomplete data can inadvertently reinforce societal prejudices. With limited text resources, generative AI tools may be unable to overcome these biases effectively, resulting in outputs that reflect or amplify societal inequalities.

SOLUTIONS AND FUTURE DIRECTIONS

Addressing the challenge of running out of text requires a multi-pronged approach. First, it is crucial to invest in research and development to enhance text generation techniques that can make the most out of limited data. Techniques such as transfer learning, data augmentation, and domain adaptation can help models generalize from smaller datasets.

Another avenue is the responsible and ethical collection and curation of text data. Collaborative efforts involving academia, industry, and regulatory bodies can ensure the availability of diverse and representative datasets, mitigating the risk of bias and maintaining the quality of AI outputs. Open access initiatives can facilitate the sharing of high-quality data, fostering innovation while preserving privacy and intellectual property rights.

Furthermore, there is a need for continuous monitoring and evaluation of AI models to detect and mitigate biases and inaccuracies. Feedback loops involving human reviewers and automated systems can help identify problematic outputs and refine the training process.

FIVE INDUSTRY USE CASES FOR GENERATIVE AI

Generative AI presents itself with five compelling use cases across various industries. One of its primary applications is in exploring diverse designs for objects, facilitating the identification of the optimal or most suitable match. This not only expedites and enhances the design process across multiple fields but also possesses the potential to introduce innovative designs or objects that might otherwise elude human discovery.

The transformative influence of generative AI is notably evident in marketing and media domains. According to Gartner’s projections, the utilization of synthetically generated content in outbound marketing communications by prominent organizations is set to surge, reaching 30% by 2025—an impressive ascent from the mere 2% recorded in 2022. Looking further ahead, a significant milestone is forecasted for the film industry, with a blockbuster release expected in 2030 to feature a staggering 90% of its content generated by AI, encompassing everything from textual components to video elements. This leap is remarkable considering the complete absence of such AI-generated content in 2022.

The ongoing acceleration of AI innovations is spawning a myriad of use cases for generative AI, spanning diverse sectors. The subsequent enumeration delves into five prominent instances where generative AI is making its mark:

Source: Gartner

NOTHING TO WORRY

Organisations see generative AI as an accelerator rather than a disruptor, but why?

Image Source: Grandview research/industry-analysis/generative-ai-market-report

Generative AI has changed from being viewed as a possible disruptor to a vital accelerator for businesses across industries in the world of technology. Its capacity to boost creativity, expedite procedures, and expand human capacities is what is driving this shift. A time-consuming job like content production can now be sped up with AI-generated draughts, freeing up human content creators to concentrate on editing and adding their own distinctive touch.

Consider the healthcare sector, where Generative AI aids in drug discovery. It rapidly simulates and analyses vast chemical interactions, expediting the identification of potential compounds. This accelerates the research process, potentially leading to breakthrough medicines.

Additionally, in finance, AI algorithms analyze market trends swiftly, aiding traders in making informed decisions. This accelerates investment strategies, responding to market fluctuations in real-time.

Generative AI’s transformation from disruptor to accelerator is indicative of its capacity to collaborate with human expertise, offering a harmonious fusion that maximizes productivity and innovation.

Image Source: Grandview research/industry-analysis/generative-ai-market-report

AI BOARDROOM FOCUS

Generative AI has taken a prominent position on the agendas of boardrooms across industries, with its potential to revolutionize processes and drive growth. In the automotive sector, for example, leading companies allocate around 15% of their innovation budgets to AI-driven design and simulation, enabling them to accelerate vehicle development by up to 30%.

Retail giants also recognize Generative AI’s impact, dedicating approximately 10% of their operational budgets to AI-powered demand forecasting. This investment yields up to a 20% reduction in excess inventory and a significant boost in customer satisfaction through accurate stock availability.

Architectural firms and construction companies channel nearly 12% of their resources into AI-generated designs, expediting project timelines by up to 25% while ensuring energy-efficient and sustainable structures.

WRAPPING UP

The warning from the UC Berkeley professor serves as a reminder of the evolving challenges faced by generative AI. The scarcity of training text poses a threat to the future development of AI models, potentially hindering their ability to generate high-quality, unbiased content. By investing in research, responsible data collection, and rigorous evaluation processes, we can mitigate these challenges and ensure that generative AI continues to push the boundaries of human creativity while being mindful of ethical considerations. As the field progresses, it is essential to strike a balance between innovation and responsible AI development, fostering a future where AI and human ingenuity coexist harmoniously.

Despite the challenges highlighted by the UC Berkeley professor, the scope of generative AI remains incredibly promising. Industry leaders and researchers are actively engaged in finding innovative solutions to overcome the text scarcity issue. This determination is a testament to the enduring value that generative AI brings to various sectors, from content creation to scientific research.

As organizations forge ahead, it is evident that the positive trajectory of generative AI is unwavering. The collaboration between AI technologies and human intellect continues to yield groundbreaking results. By fostering an environment of responsible AI development, where ethical considerations are paramount, we can confidently navigate the evolving landscape. This harmonious synergy promises a future where generative AI amplifies human potential and drives innovation to unprecedented heights.

The post The Challenge of ‘Running Out of Text’: Exploring the Future of Generative AI appeared first on Indium.

The following subjects will be covered in the blog:

• What is Google Big Query and Looker Studio?
• How can I real-time connect Big Query data with Looker Studio?
• The advantages of Looker Studio with Big Query.
• An explanation of how using Looker Studio with a Big Query client saves a tonne of time and effort.

Organisations can explore and analyse their data using the business intelligence and data analytics tool known as Looker Studio. Users can develop and share interactive dashboards, reports, and visualizations that offer insights into their data using Looker Studio. Looker Studio features a drag-and-drop user interface, simple visualization tool, and is accessible even to non-technical users.

(We have a wide range of data sources that we can connect from Looker Studio.)

The flexibility of Looker Studio to connect to a range of data sources, such as databases, cloud services, and APIs, is one of its primary advantages. Users may now quickly access and analyze data from various sources on a single platform thanks to this. Additionally, Looker Studio provides strong data modelling and transformation capabilities, enabling users to convert unstructured data into actionable insights.

Organisations can use Looker Studio to make data-driven decisions based on current data insights. They are able to spot patterns, recognize anomalies, and make well-informed judgements that promote corporate expansion and success.

Let’s now discuss Big Query. Large datasets can be quickly and affordably analyzed using Google Cloud Platform’s Big Query, a cloud-based data warehousing and querying tool. Users of Big Query may store and analyze enormous amounts of data quickly and easily without the need for a sophisticated infrastructure or on-site hardware.

One of the key benefits of Big Query is its scalability. Big Query can handle petabytes of data and can be scaled up or down as needed, making it suitable for businesses of all sizes. Big Query also supports real-time data ingestion, allowing users to analyze data as it’s generated.

Big Query is designed to be easy to use and offers a powerful SQL-like query language that allows users to quickly analyze their data. It also offers a range of integration options, including with Looker Studio, making it easy for organizations to connect and analyse their data on a single platform.

(Inbuilt function of Big Query to explore results in Looker Studio)

Overall, Looker Studio and Big Query are both powerful tools for data analytics and can help organizations make data-driven decisions. By combining the two, organizations can access real-time data insights and unlock the full potential of their data.

Visualize Big Query data with looker studio using real-time connections:

There are a variety of ways to link Big Query data to Looker Studio; in this part, we’ll focus on the most effective ones.

Using the Looker Studio Connection “Custom Query Connector”:

This is the most effective technique to visualize the results of a big query. With the Custom Query Connector, you can extract and manipulate data in ways that are not possible with regular Looker connections, making it a powerful tool for working with Big Query data in Looker. However, since it necessitates a solid grasp of SQL and database connectivity, not all users may find it appropriate. But the Data Engineers and Data Analysts can easily feel the efficiency in this.

The Big Query project and dataset, as well as the SQL query that obtains the data, must be provided in order to set up the Custom Query Connector. Once the connection is made, you can use the data to build Looker models, views, and dashboards, and you can use mechanisms like caching and data refreshing to update them in real-time.

The scheduling feature of the custom query in looker studio, runs the query in Big Query and updates the dashboard at scheduled intervals.
When dealing with large amounts of data, it will be difficult to sort and filter when we establish this from the Looker studio end, but we can implement this from either end.

(The above picture shows the options to select when we do it from the looker studio end.)

Click on Big Query data source and then select custom query, as highlighted in the above picture.

Additionally, there is a second method for using the custom query connector on the BQ end; all we need to do is use the Big Query function “explore data using looker studio” after query is executed.

(The above picture shows the options to select when we do it from the Looker Studio end from the Big Query end. )

Click on “Explore Data” and then click on “Explore with Looker Studio” as highlighted in the above picture.

It is easy and straightforward to create BI dashboards using the Custom Query connector, and we can schedule it. Later, we can add more data sources using the same query or a different query, and we can merge the data using the “Edit Connection” tool in Looker Studio.

(The above picture shows the options to select to edit the connection in Looker Studio.)

Click on “Big Query Custom SQL” under Data Function and then click on “Edit Connection” as highlighted in the above picture.
When we choose “Big Query Custom SQL” under Data Sources and then click “edit”, a pop-up window al-lowing us to “edit the connection” to the data source appears.

There are a few other ways to connect Big Query with Looker Studio.

Using Google Cloud Pub/Sub to send data updates to Looker in real-time:

With Big Query as your data source, you can set up a Cloud Function that listens for data changes in Big Query and publishes those changes to the Pub/Subtopic. Once you’ve created a Pub/Sub topic and config-ured your data source to send data updates, you’ll need to set up a Looker connection that can receive those updates. This can be done by creating a new Looker connection and configuring it to use the Pub/Sub topic as its data source.

Using cache warming process, which preloads the Looker cache with the latest data at regular inter-vals:

Cache warming is the process of preloading Looker’s cache with the latest data at regular intervals. This improves the performance of Looker dashboards and visualisations by ensuring the most up-to-date data is readily available in the cache. The process involves scheduling the cache warming, running a script to populate the cache with the latest data, monitoring the process, and tuning it for efficiency.

Looker Studio with Big Query benefits:

Real-time data visualisation: Looker Studio provides real-time access to data stored in Big Query, ena-bling users to visualise and analyse data as it is updated in real-time.

Centralised data modelling: Looker Studio enables you to develop centralised data models that can be uti-lised by numerous teams and departments within your company, ensuring accuracy and consistency in your data analysis.

Customizable dashboards: Looker Studio enables you to create customised dashboards that can be tailored to the specific needs of different teams and departments, making it easier to share insights and drive data-driven decision-making.

Easy-to-use interface: The user-friendly interface on Looker Studio makes it simple for users to construct and edit dashboards and visualisations without the need for substantial technical expertise.

Scalability: Because Looker Studio is highly scalable, you can manage significant data volumes and meet rising user demand without sacrificing performance.

Integration with other tools: Data analysis may be easily included into your current processes thanks to Looker Studio’s seamless integration with a variety of other tools and technologies, including Google Cloud Platform and a wide range of third-party applications.

Overall, Looker Studio provides a powerful, flexible, and user-friendly platform for visualising and ana-lysing data stored in Big Query, enabling organisations to gain valuable insights and make data-driven de-cisions with greater speed and accuracy.

Looker Studio with the Big Query client use case saved a lot of time and effort.

Here is a challenge we learned about from one of our clients, the largest multiplex chain in India. When the team was considering a solution to identify pipeline failures for more than 50 production pipelines, I suggested using Looker Studio and suggested that we create a metadata table that collected data on the success and failure of runs and exceptions using pub/sub messages. From this metadata table, I built a complex query using windowing fun.

Conclusion:

Now that you are more familiar with the connections, you should be able to see how we can rapidly link our Big Query data to Looker Studio. Overall, Looker Studio with Big Query provides a scalable, flexible, and user-friendly platform for visualising and analysing data, making it the ideal choice for enterprises wishing to get insights and expedite decision-making with greater speed and accuracy.

In the next blogs, you will see other unique aspects of the integration of Big Query with Looker Studio.

Gratitude for reading. I hope this is helpful.

The post Looker Studio in Real-Time synchronization with Big Query appeared first on Indium.