In a QA cycle, we capture various metrics to gauge the testing we have done against the baseline values according to industry standards. In this article, we are using an AI model to make the release sign-off decision, calculated with automated metrics.

AI-Enabled Model

AI-based release decision, often referred to as AI model deployment or rollout, involves determining when and under what conditions an AI system should be put into production or made available to end-users. Here are some key considerations for making AI-based release decisions:

Model Evaluation: Before making a release decision, it’s essential to thoroughly evaluate the AI model’s performance using appropriate metrics. This evaluation should include various aspects, such as accuracy, precision, and any other relevant performance indicators. The model should meet predefined quality and accuracy standards.

Here is the AI model designed…

Based on the above, the most important decisions are arrived at, which are mentioned below:

Release Tollgate Decision

This decision entails the criteria for Production Readiness, determining whether to sign off for production or not. The decision is based on the provided values.

Quality Quotient

The Quality Quotient is a percentage derived from established metrics used for assessing and improving software quality. The following parameters are captured, and the quality quotient is determined with a predefined formula. The decision is based on the following range of values: 0% to 98%.

Testing & Validation

Extensive testing is necessary to identify and address potential issues, including edge cases that the AI model might encounter. Testing should cover a wide range of inputs to ensure the system’s robustness. Validation involves verifying that the AI model’s performance aligns with business objectives and requirements to contribute to the desired goals.

Use Cases

This model is evaluated for two projects. One is in the social media domain, which has weekly pushes to production. We have the model with the process of capturing the status of tests and defects through tools like JIRA and qTest. The captured data is fed into a dynamic dashboard with built-in formulas for calculating the metrics needed for sign-off.

The results are greatly helpful in making the release decision. We have some feedback mechanisms which helped to evolve the model and we are recommending the same to the customer.

The second one is for a fortnightly release financial domain project. Here the model gave indicative results for making the release decision.

Release decisions should be data-driven and grounded in a well-defined process that considers the AI system’s technical and business aspects. It’s crucial to strike a balance between delivering AI solutions swiftly and ensuring they adhere to quality, ethical, and security standards. Regularly reviewing and updating the release criteria is essential as the AI system evolves and new information emerges.

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The Internet of Things (IoT) is a network of linked devices that interact with one another to carry out specific functions. Everything from smart home appliances to industrial machinery may be part of this network. The IoT has the potential to revolutionize industries and open up new business opportunities by utilizing the power of big data. As with any new technology, there are substantial obstacles that need to be overcome.

One of the biggest opportunities that big data and the IoT present is the ability to make data-driven decisions in real-time. For example, in the manufacturing industry, sensors on machinery can provide real-time data on performance, allowing for predictive maintenance and reducing downtime. Similarly, in healthcare, IoT devices can monitor patients and provide data to healthcare professionals, allowing for more personalized care.

However, with the amount of data generated by the IoT, there are also significant challenges in terms of managing, processing, and analyzing this data. Traditional data management tools and techniques are often not sufficient to handle the sheer volume of data generated by the IoT. Additionally, there are concerns around data privacy and security, as the IoT often involves sensitive data being transmitted over networks.

Here are few insights from Gartner or Forrester

According to a Gartner report, the combination of big data and the IoT presents significant opportunities for businesses, particularly in areas such as supply chain management, predictive maintenance, and customer engagement. However, the report also highlights the challenges associated with managing and analyzing the large volume of data generated by the IoT, as well as the need for businesses to ensure data security and privacy.

Similarly, a Forrester report emphasizes the potential of the IoT and big data to drive digital transformation in various industries. The report notes that businesses that effectively leverage these technologies can gain a competitive advantage by improving operational efficiency, reducing costs, and delivering better customer experiences. However, the report also warns that businesses must address challenges such as data management and security to realize the full potential of the IoT and big data.

Here are a few challenges and opportunities we should be aware of.

Opportunities:

Real-time data-driven decisions: The ability to collect and analyze real-time data from IoT devices can enable businesses to make data-driven decisions quickly and efficiently.

Increased efficiency and productivity: By using IoT devices to monitor and optimize processes, businesses can increase efficiency and productivity, leading to cost savings and increased revenue.

Improved customer experience: The IoT can be used to collect data on customer behavior and preferences, allowing businesses to offer personalized experiences and improve customer satisfaction.

New revenue streams: The IoT can open up new revenue streams for businesses by enabling them to offer new products and services, such as subscription-based models or pay-per-use models.

Challenges:

Data management: The sheer volume of data generated by IoT devices can be overwhelming for businesses, and traditional data management techniques may not be sufficient to handle it.

Data security and privacy: The IoT involves the transmission of sensitive data over networks, raising concerns around data security and privacy.

Interoperability: As the IoT involves devices from different manufacturers, there can be challenges in ensuring that these devices can communicate and work together seamlessly.

Skill gaps: As the IoT is a relatively new technology, there may be skill gaps in the workforce, making it challenging for businesses to effectively leverage it.

Use Cases:

One use case for big data and the IoT is in the transportation industry. By using IoT devices to collect data on traffic patterns and road conditions, transportation companies can optimize routes and reduce congestion. In agriculture, IoT devices can monitor soil conditions and weather patterns to optimize crop yields. In the energy industry, IoT devices can monitor power usage and detect inefficiencies, leading to cost savings and reduced carbon emissions.

How Indium Software can address

Indium Software has extensive experience in developing and implementing solutions for big data and IoT use cases. For example, our team can develop customized algorithms and machine learning models to analyze IoT data and provide real-time insights. We can also help ensure data privacy and security by implementing robust encryption and access control measures. In addition, our team can develop and deploy custom dashboards and visualizations to make it easy for businesses to understand and act on IoT data.

Here are a few real-time scenarios that illustrate how the combination of big data and the IoT is being used to drive innovation and growth across various industries:

Smart Manufacturing: A manufacturing company has implemented an IoT system to monitor and optimize its production processes in real-time. The system collects data from sensors embedded in manufacturing equipment and uses big data analytics to identify patterns and optimize production. By leveraging this technology, the company has been able to reduce downtime, increase productivity, and improve product quality.

Predictive Maintenance: A transportation company has deployed IoT sensors on its fleet of vehicles to monitor their performance and detect potential maintenance issues before they become major problems. The system collects data on factors such as engine performance, fuel consumption, and tire pressure, and uses big data analytics to identify patterns and predict maintenance needs. By leveraging this technology, the company has been able to reduce maintenance costs, increase vehicle uptime, and improve customer satisfaction.

Smart Agriculture: A farming company has implemented an IoT system to monitor and optimize its crop production processes. The system collects data from sensors embedded in soil and crop fields, as well as weather data and other environmental factors, and uses big data analytics to identify patterns and optimize crop production. By leveraging this technology, the company has been able to increase crop yields, reduce water and fertilizer usage, and improve overall farm productivity.

Wrapping Up

The potential of big data and the IoT is enormous, and businesses that can effectively leverage these technologies will have a significant advantage in the marketplace. However, it is crucial to address the challenges associated with managing and analyzing the data generated by the IoT. Indium Software has the expertise and experience to help businesses overcome these challenges and unlock the full potential of big data and the IoT.

The post Big Data’s Impact on IoT: Opportunities and Challenges in Analytics appeared first on Indium.

Cloud costs comprise about 20% of IT spending, and no one wants a shockingly high bill.
As managing cloud expenses grows, companies increasingly emphasize optimizing their cloud expenditure. According to Global Industry Analysts spending on public cloud services is expected to reach around $800 billion by 2027. However, managing costs is tricky because assets are scattered across different clouds, and most solutions only offer reactive monitoring. It’s time to bridge the gap between cost control and business success. Having a robust cloud strategy is of the utmost importance, and fintech organizations like Indium Software can assist you in managing and developing the right strategy for your cloud needs. Read how Indium can assist.

FinOps for Cloud Cost Optimization & Multi-Cloud Cost Monitoring

You have undoubtedly gotten that CALL from the CFO, Finance team, and the money inspectors from the management inquiring about your monthly cloud spending if you’ve managed a cloud data platform. And it usually comes like this:

1. Seriously, what on earth is happening with our cloud usage? Did someone leave a money faucet open?
2. Are we just flushing money down the drain with this cloud service? Did a bunch of spendthrifts infiltrate our company overnight?
3. Why is our cloud costs out of control? Do we have predictive analytics tools or solutions available to help forecast our future cloud costs?

And trailed by too many French and Latin words!

Challenges in one infographic!

FinOps is like having a financial guru for your cloud expenses. It helps you keep track of all your cloud assets and manage them from one place. You can monitor your cloud usage and see where your money is going. Predictive data analysis by co-relating cost, revenue, and business metrics assists customers in achieving unit economics and understanding how specific units and/or customers impact cloud metrics, including cost, utilization, and performance! Using FinOps, businesses can save a ton of money, around 40% to 50%, 99.95% Uptime, and 100% accurate cost center mapping, according to Gathr, while making better plans and budgets.

1. Inform: Get real-time visibility of your cloud costs and understand what’s driving them. See a detailed breakdown of your spending patterns, allocations, budgets, forecasts, and analytics.

2. Optimize: Use the insights from the “inform” phase to make intelligent adjustments. Fine-tune your configurations, find and eliminate underutilized services, uncover potential discounts, and compare costs across different workloads.

3. Operate: Put your goals into action. Share spend data with stakeholders, optimize instance sizes, establish cloud governance, and automate processes. Make cloud operations integral to your design and development, ensuring a robust and cost-effective system. Implement governance mechanisms to monitor your cloud infrastructure, estimate costs, and conduct continuous audits.

Multi-Cloud Cost Monitoring allows organizations to effectively track and manage their cloud expenses across multiple cloud platforms such as AWS, Azure, GCP, and Oracle Cloud. With Predictive Analytics for Cloud Cost Optimization!

Source: Gathr

What else do you need apart from this dashboard? Predictive Analytics > Model Building > Predictive Model > Real-Time Prediction! Read about data visualization here.

Just transform your cloud spending landscape! Our out-of-the-box cloud cost optimization solution provides enterprises with the following:

1. Unified multi-cloud cost visibility: Monitor and compare costs across AWS, Azure, Google Cloud, and Oracle Cloud in one place.

2. Granular visibility into cloud costs: Analyze costs by regions, instances, top services, resources, and operations.

3. Tag compliance: Monitor and improve cost attribution with easy visibility into tagged and untagged resources.

4. Advanced alerts & recommendations: Receive alerts for budget exhaustion, costly instances, and anomalies, with customizable thresholds and integration with enterprise tools.

5. Improved cloud operations and ROI: Optimize containerized workloads, monitor Infra as Code pipelines, and streamline Kubernetes applications for increased automation and observability.

Predictive analysis can be useful for banks to predict customer behavior and preferences. This can help decide what products can be sold to which category of customers and help improve customer experience. It can also predict market fluctuations which help organizations address issues at the right time to get the best outcome. Predictive analysis can also help prevent fraudulent transactions by blocking suspicious access to a customer’s account. Based on customer credit scores, it can predict which customers are likely to miss payments and whom to lend money to, and it plays a significant role in gaining a competitive advantage and helps in better decision-making. Read here

Where to go from here?

All of the above are crucial for effective cost control, regardless of whether you utilize third-party tools, your CDP’s features, a customized set of services, or a combination. It’s rare to find a single solution that solves every problem. I highly recommend forming your team to put the right resources in place to monitor, surface, and optimize cloud costs and usage.

The next time you have a conversation with the CFO, they might say, “Fantastic job! Look at the money we saved this month!”

The post Scaling Up or Down: How Predictive Analytics and FinOps Can Optimize Your Cloud Spending appeared first on Indium.

In addition, data is increasingly being used to power artificial intelligence (AI) and machine learning (ML) systems, which are driving innovation and transforming businesses across various industries. AI and ML systems require large amounts of high-quality data to train models, make predictions, and automate processes. As such, companies are investing heavily in data infrastructure and analytics capabilities to harness the power of data.

Data is also a highly valuable resource because it is not finite, meaning that it can be generated, shared, and reused without diminishing its value. This creates a virtuous cycle where the more data that is generated and analyzed, the more insights can be gained, leading to better decision-making, increased innovation, and new opportunities for growth. Thus, data has become a critical asset for businesses and governments alike, driving economic growth and shaping the digital landscape of the 21st century.

There are various data storage methods in data science, each with its own strengths and weaknesses. Some of the most common data storage methods include:

• Relational databases: Relational databases are the most common method of storing structured data. They are based on the relational model, which organizes data into tables with rows and columns. Relational databases use SQL (Structured Query Language) for data retrieval and manipulation and are widely used in businesses and organizations of all sizes.

• NoSQL databases: NoSQL databases are a family of databases that do not use the traditional relational model. Instead, they use other data models such as document, key-value, or graph-based models. NoSQL databases are ideal for storing unstructured or semi-structured data and are used in big data applications where scalability and flexibility are key.

• Data warehouses: Data warehouses are specialized databases that are designed to support business intelligence and analytics applications. They are optimized for querying and analyzing large volumes of data and typically store data from multiple sources in a structured format.

• Data lakes: Data lakes are a newer type of data storage method that is designed to store large volumes of raw, unstructured data. Data lakes can store a wide range of data types, from structured data to unstructured data such as text, images, and videos. They are often used in big data and machine learning applications.

• Cloud-based storage: Cloud-based storage solutions, such as Amazon S3, Microsoft Azure, or Google Cloud Storage, offer scalable, secure, and cost-effective options for storing data. They are especially useful for businesses that need to store and access large volumes of data or have distributed teams that need access to the data.

To learn more about : How AI and ML models are assisting the retail sector in reimagining the consumer experience.

Data collection is an essential component of data science and there are various techniques used to collect data. Some of the most common data collection techniques include:

• Surveys: Surveys involve collecting information from a sample of individuals through questionnaires or interviews. Surveys are useful for collecting large amounts of data quickly and can provide valuable insights into customer preferences, behavior, and opinions.

• Experiments: Experiments involve manipulating one or more variables to measure the impact on the outcome. Experiments are useful for testing hypotheses and determining causality.

• Observations: Observations involve collecting data by watching and recording behaviors, actions, or events. Observations can be useful for studying natural behavior in real-world settings.

• Interviews: Interviews involve collecting data through one-on-one conversations with individuals. Interviews can provide in-depth insights into attitudes, beliefs, and motivations.

• Focus groups: Focus groups involve collecting data from a group of individuals who participate in a discussion led by a moderator. Focus groups can provide valuable insights into customer preferences and opinions.

• Social media monitoring: Social media monitoring involves collecting data from social media platforms such as Twitter, Facebook, or LinkedIn. Social media monitoring can provide insights into customer sentiment and preferences.

• Web scraping: Web scraping involves collecting data from websites by extracting information from HTML pages. Web scraping can be useful for collecting large amounts of data quickly.

Data analysis is an essential part of data science and there are various techniques used to analyze data. Some of the top data analysis techniques in data science include:

• Descriptive statistics: Descriptive statistics involve summarizing and describing data using measures such as mean, median, mode, variance, and standard deviation. Descriptive statistics provide a basic understanding of the data and can help identify patterns or trends.

• Inferential statistics: Inferential statistics involve making inferences about a population based on a sample of data. Inferential statistics can be used to test hypotheses, estimate parameters, and make predictions.

• Data visualization: Making charts, graphs, and other visual representations of data to better understand patterns and relationships is known as data visualization. Data visualization is helpful for expressing complex information and spotting trends or patterns that might not be immediately apparent from the data.

• Machine learning: Machine learning involves using algorithms to learn patterns in data and make predictions or decisions based on those patterns. Machine learning is useful for applications such as image recognition, natural language processing, and recommendation systems.

• Text analytics: Text analytics involves analyzing unstructured data such as text to identify patterns, sentiment, and topics. Text analytics is useful for applications such as customer feedback analysis, social media monitoring, and content analysis.

• Time series analysis: Time series analysis involves analyzing data over time to identify trends, seasonality, and cycles. Time series analysis is useful for applications such as forecasting, trend analysis, and anomaly detection.

Use Cases

To illustrate the importance of data in AI and ML, let’s consider a few use cases:

• Predictive Maintenance: In manufacturing, AI and ML can be used to predict when machines are likely to fail, enabling organizations to perform maintenance before a breakdown occurs. To achieve this, the algorithms require vast amounts of data from sensors and other sources to learn patterns that indicate when maintenance is necessary.
• Fraud Detection: AI and ML can also be used to detect fraud in financial transactions. This requires large amounts of data on past transactions to train algorithms to identify patterns that indicate fraudulent behavior.
• Personalization: In e-commerce, AI and ML can be used to personalize recommendations and marketing messages to individual customers. This requires data on past purchases, browsing history, and other customer behaviors to train algorithms to make accurate predictions.

Real-Time Analysis

To achieve optimal results in AI and ML applications, data must be analyzed in real-time. This means that organizations must have the infrastructure and tools necessary to process large volumes of data quickly and accurately. Real-time analysis also requires the ability to detect and respond to anomalies or unexpected events, which can impact the accuracy of the algorithms.

Wrapping Up

In conclusion, data is an essential component of artificial intelligence (AI) and machine learning (ML) applications. Collecting, storing, and analyzing data effectively is crucial to maximizing the performance of AI and ML systems and obtaining optimal results. Data visualization, machine learning, time series analysis, and other data analysis techniques can be used to gain valuable insights from data and make data-driven decisions.

The post Maximizing AI and ML Performance: A Guide to Effective Data Collection, Storage, and Analysis appeared first on Indium.