AI-300 Certification

Microsoft has officially introduced the AI-300 certification, and it marks one of the biggest shifts yet in how Azure AI professionals are validated. Where the older DP-100 track focused on building and training models inside notebooks, AI-300: Operationalizing Machine Learning and Generative AI Solutions is built for engineers who take those models and generative AI agents into production and keep them running reliably at scale.

 This is Microsoft's answer to a very real industry gap: organizations don't just need people who can train a model, they need engineers who can operationalize it. AI-300 sits at the intersection of two disciplines Microsoft now bundles under one banner, AI Operations (AIOps) — traditional MLOps for classic machine learning models, and the newly formalized GenAIOps for generative AI apps and autonomous agents built on Microsoft Foundry.

At SkillTech Club, we've built this course around the official exam blueprint rather than generic theory. You'll work hands-on with Azure Machine Learning, GitHub Actions, Azure CLI and Bicep to automate infrastructure and you'll learn how to deploy, evaluate, monitor and optimize generative AI systems the way production teams actually do it — with CI/CD pipelines, observability dashboards and safety evaluations, not one-off scripts.

If you've already explored our AI-103 Certification  course and want to go one level deeper into runnin* AI systems in production rather than just building them, AI-300 is your natural next step. 

This course is mapped directly to the five official Skills Measured domains published by Microsoft, so your prep time goes exactly where the exam weight is:

Design and Implement MLOps Infrastructure: Provision secure, scalable Azure Machine Learning workspaces, configure managed identities and role-based access control (RBAC), set up private networking and automate infrastructure deployment using Bicep templates and the Azure CLI.

Implement Machine Learning Model Lifecycle and Operations: Manage the complete journey of a model — training orchestration, MLflow experiment tracking, model registration and versioning, validation, deployment strategies (including blue-green rollouts), rollback procedures and production monitoring for data drift.

Design and Implement GenAIOps Infrastructure: Deploy foundation models through Microsoft Foundry using serverless API endpoints and managed compute, version prompts and build CI/CD pipelines using GitHub Actions for generative AI applications and agents.

Implement Generative AI Quality Assurance and Observability: Run safety and groundedness evaluations, set up observability and logging for LLM applications and build automated quality gates before production release.

Optimize Generative AI Systems and Model Performance: Tune retrieval-augmented generation (RAG) pipelines by adjusting chunk sizes and similarity thresholds, select and fine-tune embedding models, implement hybrid (semantic + keyword) search and optimize models for cost and latency in production.