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ACR Automatically Triggered Tasks connect Azure Container Registry directly to your Git repository — every code push fires a build automatically, no manual CLI commands required. In this hands-on lab, you will link ACR to an Azure DevOps repo, create a triggered task, and watch a container image build and push itself on every commit.
ACR Quick Tasks lets you build and push container images to Azure Container Registry entirely in the cloud — no Docker Desktop required. In this hands-on lab, you will create a registry, write a Dockerfile, and run az acr build to see exactly how Azure handles the entire build pipeline for you.
Azure Container Registry Tasks (ACR Tasks) is the cloud-native answer to one of the most common pain points in containerized development — inconsistent builds caused by different Docker Desktop versions across developer machines. With ACR Tasks, you offload the entire build, test, and push pipeline to Azure itself — no Docker installation required. This AI-200 certification guide covers Quick Tasks, Triggered Tasks, Multi-Step Tasks, Managed Identity authentication, and the best practices Microsoft expects you to implement for containerized AI workloads on Azure.
Microsoft has officially announced that AZ-204: Developing Solutions for Microsoft Azure will retire on July 31, 2026. Its replacement — AI-200: Developing AI Cloud Solutions on Azure — marks a fundamental shift in what Microsoft expects Azure developers to know. This is not a minor update. The new Microsoft Certified: Azure AI Cloud Developer Associate credential moves beyond App Service and Blob Storage to focus on containerised AI workloads, vector databases, Azure OpenAI integration, and event-driven pipelines. Whether you’re mid-prep for AZ-204 or starting fresh, here’s everything you need to know about the transition.
Pip: If you have ever manually copied a report from a dev workspace to production and immediately regretted it, Praveen
Choosing the right AI model for your application doesn’t have to be guesswork. Microsoft Azure AI Foundry provides a powerful, end-to-end platform to deploy, manually compare, and automatically evaluate large language models using industry-standard quality and safety metrics. In this guide, we walk through the complete workflow — deploying GPT-4.1 and GPT-4.1-mini, running side-by-side playground comparisons, and setting up automated evaluations that score your models on Groundedness, Coherence, Relevance, Fluency, and DeflectionRate. The result? Data-driven confidence in your AI model selection.
The Operate tab in Microsoft Azure AI Foundry is your command center for managing AI at enterprise scale. From monitoring fleet health and tracking agent performance, to enforcing compliance policies and managing token quotas — everything you need to govern your AI resources lives in one place. This article walks through each of the five menu items: Overview, Assets, Compliance, Quota, and Admin.
Microsoft Foundry is a unified platform that brings together everything developers, data scientists, and AI engineers need to build, deploy, and govern AI applications. While the Discover tab is where you explore what is available, the Build tab is where the real work happens.
The Build tab is your construction workspace. It is where you create and manage the actual assets of your AI project: the agents that automate tasks, the models that power them, the tools that extend them, the knowledge that grounds them, and the safeguards that protect them.
The Discover tab in Microsoft Foundry is your exploration hub for everything AI — models, agents, tools, and solution templates, all in one place. Whether you’re evaluating your first model from a catalog of 11,000+, browsing pre-built agent starters for enterprise use cases, or looking for an end-to-end solution template to accelerate your build, the Discover tab is where the journey begins.
Microsoft Foundry is Azure’s boldest move yet in enterprise AI — a unified platform that brings models, agents, tools, and governance under a single roof. Gone are the days of juggling five SDKs, multiple endpoints, and disconnected billing models. With access to over 1,900 models from OpenAI, Anthropic, Mistral, Meta, and more, Foundry gives development teams one consistent API, one portal, and one way to ship production AI — faster than ever before.
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