Systems | Development | Analytics | API | Testing

This post was originally published in June 2023 and has been updated for comprehensiveness. Steve Jobs famously remarked that the way to boost programmer productivity is to create apps with less code. The market has finally caught up with his vision.

The world of business intelligence (BI) and analytics has evolved from descriptive to predictive, and now, to generative. Generative AI, a cutting-edge technology, is making its mark in embedded analytics solutions. These powerful AI analytics tools are assisting business users in analyzing their data sources more quickly and easily, generating new data sets for deeper insights, and helping make the embedded BI experience more accessible for more people.

Artificial intelligence (AI) is changing the software development landscape, particularly in the quality assurance (QA) domain. According to a market study, the AI market is projected to reach US$826.7 billion by 2030, driven by a CAGR of 28.46% and widely adopted across industries globally. Speed in today’s market cannot be overemphasized because the market demands faster and much better quality software.

In the fast-paced world of software development, time-to-market is everything. With AI and generative AI tools making their mark, developers now have the power to reduce development time drastically while maintaining high code quality. One such game-changing application? AI-powered VS Code extensions for unit testing. These intelligent extensions are not just tools; they’re enablers, helping teams ship features faster, reduce bugs, and maintain confidence in their code.

In a significant stride toward transforming AI infrastructure, ClearML has recently announced a collaboration with AMD. By integrating with AMD’s powerful hardware and open-source ROCm software with ClearML’s silicon-agnostic, end-to-end platform, we’re empowering IT teams and AI builders to innovate with ease across diverse infrastructures and integrate GPUs from multiple vendors.

In Part I of this blog series, we laid the foundation for understanding how data fuels AI and why having the right data at the right time is essential for success. We explored the basics of AI, including its reliance on structured and unstructured data, and how streaming data can help unlock its full potential.

Welcome to this three-part blog series where we explore how to transform generative AI (GenAI) into a powerful, real-time solution that drives business success.

Businesses want to connect with their audiences through mobile applications; therefore, they have become the lifeline. When millions of apps compete for attention, the smallest glitch can frustrate users, dissatisfied with products, and otherwise be abandoned. Delivering a highly secure, seamless, and high-performance app that users want to use every day requires specialty and sound testing approaches. Outsourcing is a smart decision, given that it delivers seamless, high-quality apps.

In today’s data-driven world, Large-scale error log management is essential for maintaining system functionality. It can be quite difficult to pinpoint the underlying causes of problems and come up with workable solutions when you're working with hundreds of thousands of logs, each of which contains a substantial amount of data. Thankfully, automating this process using fine-tuned AI models—like those from OpenAI—makes it more productive and efficient.

Snowflake is one of the top cloud data warehouses. Regardless of the many documentations available, I have personally faced issues while carrying out Snowflake CDC (Change data capture). Therefore, I thought sharing everything a data practitioner should know about this before you start would be helpful. Let’s jump right into it!