AI-300 : Operationalize machine learning and generative AI solutions

This course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It 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

Learning Path 1: Operationalize machine learning and generative AI solutions

• Design a machine learning training solution

• Experiment with Azure Machine Learning

• Optimize model training in Azure Machine Learning

• Perform hyperparameter tuning with Azure Machine Learning

Learning Path 2: Operationalize machine learning and generative AI solutions

• Run pipelines in Azure Machine Learning

• Plan and prepare an MLOps solution with Azure Machine Learning

• Automate model training with GitHub Actions

• Deploy and monitor a model in Azure Machine Learning

Learning Path 3: Operationalize machine learning and generative AI solutions

• Plan and prepare a GenAIOps solution

• Manage prompts for agents in Microsoft Foundry with GitHub

• Evaluate and optimize AI agents through structured experiments

Learning Path 4: Operationalize machine learning and generative AI solutions

• Automate AI evaluations with Microsoft Foundry and GitHub Actions

• Implement observability and monitoring for generative AI workloads

• Optimize and fine-tune AI agents for production