Systems | Development | Analytics | API | Testing

It's not even 9 AM, and the day's already started. A customer can't reset their password. The product team just found a bug in the app. A partner bank needs data synced ASAP. Oh, and the CEO wants to know when the mobile app can do instant loans. Sounds about right? Yeah, for banking leaders, this is just another Tuesday. Customers want everything to be easy and digital. Stakeholders want things done well, safely, and by the book. And nobody has time for old-school stuff.

APIs have become the backbone of communication between applications in today’s software-oriented world. Digital systems are increasingly complex due to the proliferation of microservices and distributed systems, making API testing both efficient and crucial. A recent study projects the API testing industry to grow from USD 4.92 billion in 2025 to USD 27.38 billion by 2034, reflecting a compound annual growth rate (CAGR) of 20.99% during the 2025-2034 timeline. Source: Market Research Future.

AI coding assistants are transforming the way developers approach software development by automating routine tasks and enhancing code quality. These tools leverage artificial intelligence and machine learning to provide real-time code suggestions, auto-complete functions, and even debug existing code, making the development process faster and more accurate.

As LLMs become central to AI-driven products like copilots and customer support chatbots, data science teams need to ensure the LLM performs well for the use case. The process of LLM evaluation ensures reliability, safety and performance in production AI systems. In this guide, we explore how to approach evaluations across development and production lifecycles, what frameworks to use, and how the integration between open-source MLRun and Evidently AI enables more scalable, structured testing.

Playlists for every mood and occasion. Media recommendations grouped by the most niche theme from your watch history. Sophisticated ad algorithms that optimize pay-per-click ads for the customer experience. Whether you call them digital-native, disruptors, or just tech giants, the likes of Spotify, Netflix, and Amazon have long made uncannily personal experiences a key part of their differentiation or business models.

Coding isn’t just a chore anymore – it is an experience. With the rise and advancement of AI-driven coding tools, developers are now able to enjoy what Andrej Karpathy calls "vibe coding" – a state where we turn off our control and let the tools vibe with us. In this blog post we are going to discuss how we can tap into vibe coding to ship faster, feel creative, and stay in flow.

Large Language Models (LLMs) like ChatGPT and Claude have revolutionized how we think about business automation and conversational interfaces. So it’s no surprise that many organizations are considering building their own LLM-powered chatbot. But here’s the truth: creating a secure, scalable, and intelligent chatbot from scratch is harder than it looks.

ClearML Enterprise v3.26 brings powerful upgrades across model deployment, NIMs container deployment, and dataset management – all part of our end-to-end platform for managing and scaling AI in the enterprise.

Our Advanced CI team is always trying to push what we have further, which often means we have to support APIs and code that are not yet fully stable. On the other hand, we are bound to support projects that are using deprecated code to a certain extent. This is particularly true for build systems that are rapidly changing to support the needs of application developers, such as Gradle.