Module 1: Make Data Available in Azure Machine Learning

• Understand URIs and their use in Azure Machine Learning.
• Create and manage datastores and data assets.
• Exercise: Make data accessible in Azure Machine Learning.

Module 2: Work with Compute Targets in Azure Machine Learning

• Select appropriate compute targets for specific tasks.
• Configure and use compute instances and clusters.
• Exercise: Work with compute resources in Azure Machine Learning.

Module 3: Work with Environments in Azure Machine Learning

• Understand curated and custom environments in Azure Machine Learning.
• Create and use custom environments for ML workflows.
• Exercise: Manage and work with environments.

Module 4: Run a Training Script as a Command Job

• Convert notebooks into executable scripts.
• Execute scripts as command jobs with parameters.
• Exercise: Run training scripts as command jobs.

Module 5: Track Model Training with MLflow in Jobs

• Track and monitor model training metrics using MLflow.
• Evaluate model performance through MLflow logs.
• Exercise: Use MLflow to track training jobs.

Module 6: Register an MLflow Model in Azure Machine Learning

• Log and register models using MLflow.
• Understand the MLflow model format and its integration with Azure.
• Exercise: Log and register MLflow models in Azure Machine Learning.

Module 7: Deploy a Model to a Managed Online Endpoint

• Explore managed online endpoints for deploying ML models.
• Deploy MLflow models to managed online endpoints.
• Test and validate endpoint functionality.
• Exercise: Deploy and test MLflow models on managed online endpoints.

This course provides the practical knowledge and hands-on experience needed to efficiently utilize Azure Machine Learning for end-to-end machine learning workflows, ensuring participants are equipped to handle real-world data science and engineering challenges.