Systems | Development | Analytics | API | Testing

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Ensuring Accuracy and Reliability with ML Model Validation

As demand for machine learning (ML) grows, rigorous testing and quality assurance are crucial. ML models need quality training data and robust algorithms. Without thorough testing, inaccurate outcomes can occur, especially in sectors like healthcare, finance, and transportation. A 2023 ScienceDirect report found data leakage in 294 academic publications across 17 disciplines, highlighting the need to address this issue in ML-based science.

Why It's Time to Update Your Embedded Analytics

After 20 years of dashboards, today’s line-of-business users expect more value from their analytics, and it’s up to your business to keep updated - or risk getting left behind. There was a time when product teams could purchase basic dashboards and data visualizations, and that was more than enough to satisfy the average user’s business intelligence (BI) and analytics needs. Today, however? Not so much.

Choosing the Right Chart Type for Good Data Visualization

An effective dashboard requires careful design to present data in the best way, and to help more people (users, customers) find insights without feeling overwhelmed. Yellowfin BI comes with a wide variety of chart types as part of its extensive data visualization tools, and while it is tempting to use a lot of eye-catching charts to make a dashboard that looks great, it is important to select the right chart for the right situation.

MySQL vs MS SQL Server: Key Similarities and Differences | Dreamfactory

Today, we're looking at MySQL vs MS SQL Server. Relational database management systems (RDBMS) form the backbone of enterprise IT. The main difference between MySQL and Microsoft SQL Server is that MySQL is an open-source RDBMS known for its cross-platform compatibility and cost-effectiveness, while Microsoft SQL Server is a commercial RDBMS primarily designed for the Windows platform, offering advanced features and tight integration with Microsoft technologies.

Using Moesif, AWS, and Stripe to Monetize Your AI APIs Part-2: Setting up Metering and API Access

In the previous article, we set up the AI API with AWS Lambda and Gateway, integrated it with Moesif, and then connected Stripe with Moesif. We now have the infrastructure to begin billing for API usage. In this article, we move on to configuring Moesif with the following steps in the API monetization journey: First, let’s set the prices we want to charge for API usage in Moesif.

Save Time and Improve the Accuracy of Your NetSuite Reporting

Financial and operational reporting for NetSuite can be a challenge. As is the case with many ERP systems, NetSuite’s reporting capabilities tend to be somewhat restrictive. It can be difficult to pull information from multiple NetSuite modules into a single, cohesive report. In other instances, information for which there ought to be a fairly straightforward reporting process turns out to be inaccessible.

The 8 Best API Documentation Examples for 2024 | Dreamfactory

Your API documentation is just as important as your API. It defines how easy it is for users to learn, understand, and use your open-source or paid-for product. In this post, DreamFactory highlights eight of the best API documentation examples from well-known tools. You can use these examples for inspiration when creating your API docs. Here are the key takeaways to know about each of these API documentation examples.

Mastering Data Compliance: Tips, Strategies, and Best Practices

Data has become the lifeblood of businesses across all industries. With the exponential growth of data collection and processing, the importance of data compliance has skyrocketed. And navigating this complex business landscape is vital for any organization handling sensitive information. 5 key takeaways from this post on mastering data compliance are.

The Do's and Don'ts of Regression Testing

The modern-day SDLC methodologies such as agile, CI/CD, and DevOps are flexible enough to incorporate change requests in each sprint, which increases the probability of introducing errors in existing functionality. This makes validating existing functionality, detecting newly introduced bugs, and resolving them mandatory in each build release. Whether manual or automated, such software testing is widespread and referred to as regression testing.