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

Move AI from experimentation into reliable operation

An AI model is only useful at scale when teams can deploy it safely, track its performance and update it consistently. This four-day course develops the operational skills needed by machine learning engineers, data scientists and DevOps professionals to support production AI on Azure. It combines MLOps for traditional models with GenAIOps for generative applications and agents.

Build repeatable machine learning workflows

Explore how Azure Machine Learning workspaces, compute, datastores and reusable assets support shared development. Understand how MLflow, training pipelines and registries help teams track experiments, manage versions and promote models. Examine automated provisioning and delivery with GitHub Actions, Bicep and Azure CLI, including the controls needed for secure environments.

Operate generative AI and agents

Learn how Microsoft Foundry supports model deployment, prompt versioning and controlled releases. Consider the operational implications of foundation-model choice, throughput and application architecture. Explore automated evaluation and observability, including groundedness, relevance, safety, latency, token use and tracing, so issues can be identified before they affect users.

Improve quality, cost and reliability

Examine production monitoring, data drift, retraining triggers and rollback strategies. Review how retrieval settings, embedding models and fine-tuning affect generative AI quality and efficiency. Connect these activities to responsible AI, access management and collaboration between data science and platform teams.

Prepare for the AI-300 exam

The course follows Microsoft's current five exam domains, from MLOps infrastructure and model lifecycle management to GenAIOps quality assurance and optimisation. It supports certification preparation, while further technical practice may be required before attempting the assessment.