Systems | Development | Analytics | API | Testing

Pre-Packaged Inference, Production-Grade: AMD AIMs with ClearML

Running production LLM inference on a new accelerator family is a layered problem. The model matters. The runtime that exists for the GPU you have matters at least as much. So does the precision mode that works without losing accuracy, the inference engine that hits your throughput targets, and the secure endpoint the rest of your stack can actually call. The entire stack underneath the model is where most of the real engineering work lives and where the cost of getting it wrong shows up first.

Explainable AI in Customer-Facing Analytics: How Yellowfin Turns Predictions into Action

Predictions alone are no longer enough. A churn score is not useful if no one trusts it, and a risk score does not help if the next step is unclear. The same goes for a recommendation engine. People need to know why a model made a call, and what action comes next. That is the core shift in explainable AI for analytics. The work has moved from “what happened?” to “why did it happen, and what should I do now?” Customer-facing analytics depends on that shift.

Architecting Reliable AI: The Complete Technical Framework for Multi-Agent System Testing

The conversation around AI validation has rapidly outgrown simple prompt engineering and single-turn model checks. While the industry spent the last few years establishing baseline protocols for individual AI agent testing, enterprise automation has already advanced to the next engineering frontier: the Multi-Agent System (MAS).

Navigational Perception in Legal Information Environments

Legal digital environments operate within a unique informational context where clarity, trust, and accessibility must coexist with complexity. Unlike many commercial websites that focus primarily on transactions or engagement, legal platforms often serve as information systems that help users understand unfamiliar situations, evaluate options, and make important decisions. To support this process, legal environments rely on layered information architecture, where content is organized into interconnected informational nodes.

Your AI Projects Need a Platform

In my younger days, eons ago in tech years, I worked on many enterprises IT projects or saw them up close. Failure rates of these projects were incredibly high. There was a mortgage system that was expected to be live in six months but ended up taking over five years and went live with a small fraction of the features originally planned. Many other projects never got out of the development phase.

The Hidden Cost of AI Testing: Stop Burning LLM Tokens in Your CI/CD Pipeline

AI testing against live LLM APIs can quietly drive massive token costs across development, QA, and CI/CD pipelines. Every test execution consumes real tokens—at production rates—creating hidden, variable costs that scale with your AI adoption. In this video, discover how leading enterprises are eliminating LLM token spend using service virtualization. Learn how BlazeMeter intercepts API calls, simulates realistic AI responses (completions, embeddings, and large payloads), and enables full-scale testing without invoking live models.

Streaming highlights from Databricks Data + AI Summit

Join Tun Shwe and Jeremy Frenay as they stream live from the floor of the Databricks Data + AI Summit! They’ll break down the biggest announcements, key takeaways, and cutting-edge trends shaping the intersection of AI and data streaming. Register to get an insider look at the future of data AI streaming.

Guessing AI vs. Verifiable AI: Why the Difference Matters in Finance

I asked Claude what the cash position would be at year-end. The answer was about 30% off. A CFO said this at a finance leaders breakfast in Prague. Almost every CFO in the room had a version of the same story. The problem is not the model. Claude is not bad at maths. The problem is what the model was reasoning over - raw financial data with no governed definitions, no intercompany rules, no agreed methodology for what 'cash position' means at that specific company.

WWDC 2026: Device Hub and what it means for CI/CD

At WWDC 2026, Apple shipped a long list of changes, and we covered the ones flying under the radar in our round-up of the less-reported announcements. One of them deserves a closer look on its own: the way Xcode 27 reshapes how developers manage devices and simulators. Xcode 27 ships with a new app called Device Hub, replacing Simulator.app found in older Xcodes. Device Hub is where both physical devices and simulators can be managed from now on.

What is SonarQube and how does it work?

SonarQube is a code quality and security platform that helps teams detect bugs, vulnerabilities, and maintainability issues early in development, using static code analysis rather than manual reviews. SonarQube fits directly into modern workflows, integrating with CI/CD pipelines and development environments to continuously verify code through quality gates, dashboards, and automated checks. And in this guide, we’ll give you.