AI-300 focuses on operationalizing machine learning and generative AI on Azure—covering MLOps, GenAIOps, automation, deployment, monitoring, and optimization of production AI systems.This course covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices. Module 1: Operationalize machine learning models (MLOps)Experiment with Azure Machine LearningPerform hyperparameter tuning with Azure Machine LearningRun pipelines in Azure Machine LearningTrigger Azure Machine Learning jobs with GitHub ActionsTrigger GitHub Actions with feature-based developmentWork with environments in
