Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300)

Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300) 

The Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300) certification is designed for professionals who want to validate their expertise in building, automating, deploying, and monitoring AI operations solutions on Azure. This certification focuses on machine learning operations (MLOps) and generative AI operations (GenAIOps), together forming modern AI operations (AIOps) environments. It is ideal for professionals responsible for creating scalable AI infrastructure using Azure Machine Learning, Microsoft Foundry, GitHub Actions, and infrastructure as code (IaC) practices.

Knowledge Gained

By preparing for the AI-300 certification, you will gain expertise in:

• Designing Azure MLOps and GenAIOps infrastructure
• Automating training, deployment, and monitoring pipelines
• Managing ML model versioning and lifecycle operations
• Implementing prompt evaluation and GenAI observability
• Optimizing RAG pipelines, embeddings, and retrieval
• Using GitHub Actions, Bicep, and Azure CLI for AI automation
• Monitoring latency, token usage, cost, and drift
• Improving LLM and ML model performance in production

Skills Required

To succeed in AI-300, candidates should be comfortable with:

• Python programming
• Machine learning workflows
• Azure Machine Learning
• Microsoft Foundry
• GitHub Actions
• Infrastructure as code using Bicep
• Azure CLI and command-line workflows
• Model evaluation and monitoring concepts
• RAG, embeddings, and prompt optimization
• Entry-level DevOps practices

Course Outline 

The Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300) Exam covers the following - 

• Domain 1 - Design and implement an MLOps infrastructure (15–20%)
• Domain 2 - Implement machine learning model lifecycle and operations (25–30%)
• Domain 3 - Design and implement a GenAIOps infrastructure (20–25%)
• Domain 4 - Implement generative AI quality assurance and observability (10–15%)
• Domain 5 - Optimize generative AI systems and model performance (10–15%)