Why Cost Optimization in Azure AI Services Matters? Cost optimization ensures that AI projects remain scalable, predictable, and sustainable, balancing innovation with financial responsibility. A...

Deploying large language models (LLMs) without rigorous evaluation is risky: quality regressions, safety issues, and expensive rework often surface in production—when it’s hardest to fix. This guide ...

In this blog post, we’ll explore the Voice Live API from Azure AI Foundry. Officially released for general availability on October 1, 2025, this API unifies speech recognition, generative AI, and tex...

Introduction Modern development workflows increasingly rely on secure integrations between tools and platforms. Copilot Studio, with its ability to extend functionality through MCP (Model Context P...

Software delivery is accelerating while governance, AI adoption, and supply-chain risk get more complex. Teams need a shared, verifiable baseline across Git, CI/CD, Copilot, platform administration, ...

As a Microsoft Technical Trainer, you get to know many interesting people. But one of the most famous personalities I have met in a long time is Chatty—the GitHub CoPilot AI model helping millions of...

Observability in Azure AI: From Black Box to Transparent Intelligence If you are an AI developer or an engineer, you can benefit from Azure AI observability by gaining deep visibility into agent be...

Modern enterprise systems face a simple problem: how to make AI decisions reliable, explainable, and production-ready? This post walks through how the Microsoft Agent Framework structures AI-driven...

When deploying large language models in Azure AI Foundry, does selecting PTUs (Provisioned Throughput Units) save you money? This is the kind of article that might get its humble writer in hot water,...