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Module 1: Design a data ingestion strategy for machine learning projects  Learn how to design a data ingestion solution for training data used in machine learning projects. After completing this module, students will be able to:

• Identify your data source and format
• Choose how to serve data to machine learning workflows
• Design a data ingestion solution

Module 2: Design a machine learning model training solution  Learn how to design a model training solution for machine learning projects. After completing this module, students will be able to:

• Identify machine learning tasks
• Choose a service to train a model
• Choose between compute options

Module 3: Design a model deployment solution  Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model. After completing this module, students will be able to:

• Understand how a model will be consumed
• Decide whether to deploy your model to a real-time or batch endpoint

Module 4: Design a machine learning operations solution Learn about machine learning operations or MLOps to bring a model from development to production. Identify options for monitoring and retraining when preparing a model for production. After completing this module, students will be able to:

• Explore an MLOps architecture
• Design for monitoring
• Design for retraining

Module 5: Explore Azure Machine Learning workspace resources and assets  As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets. After completing this module, students will be able to:

• Create an Azure Machine Learning workspace.
• Identify resources and assets
• Train models in the workspace

Module 6: Explore developer tools for workspace interaction  Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2). After completing this module, students will be able to:

• The Azure Machine Learning studio
• The Python Software Development Kit (SDK)
• The Azure Command Line Interface (CLI)

Module 7: Make data available in Azure Machine Learning  Learn about how to connect to data from the Azure Machine Learning workspace. You'll be introduced to datastores and data assets. After completing this module, students will be able to:

• Work with Uniform Resource Identifiers (URIs)
• Create and use datastores
• Create and use data assets

Module 8: Work with compute targets in Azure Machine Learning  Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster. After completing this module, students will be able to:

• Choose the appropriate compute target
• Create and use a compute instance
• Create and use a compute cluster

Module 9: Work with environments in Azure Machine Learning  Learn how to use environments in Azure Machine Learning to run scripts on any compute target. After completing this module, students will be able to:

• Understand environments in Azure Machine Learning
• Explore and use curated environments
• Create and use custom environments

Module 10: Find the best classification model with Automated Machine Learning  Learn how to find the best classification model with automated machine learning (AutoML). You'll use the Python SDK (v2) to configure and run an AutoML job. After completing this module, students will be able to:

• Prepare your data to use AutoML for classification
• Configure and run an AutoML experiment
• Evaluate and compare models

Module 11: Track model training in Jupyter notebooks with MLflow  Learn how to use MLflow for model tracking when experimenting in notebooks. After completing this module, students will be able to:

• Configure to use MLflow in notebooks
• Use MLflow for model tracking in notebooks

Module 12: Run a training script as a command job in Azure Machine Learning  Learn how to convert your code to a script and run it as a command job in Azure Machine Learning. After completing this module, students will be able to:

• Convert a notebook to a script
• Test scripts in a terminal
• Run a script as a command job
• Use parameters in a command job

Module 13: Track model training with MLflow in jobs Learn how to track model training with MLflow in jobs when running scripts. After completing this module, students will be able to:

• Use MLflow when you run a script as a job
• Review metrics, parameters, artifacts, and models from a run

Module 14: Perform hyperparameter tuning with Azure Machine Learning Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning. After completing this module, students will be able to:

• Define a hyperparameter search space
• Configure hyperparameter sampling
• Select an early-termination policy
• Run a sweep job

Module 15: Run pipelines in Azure Machine Learning  Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows. After completing this module, students will be able to:

• Create components
• Build an Azure Machine Learning pipeline
• Run an Azure Machine Learning pipeline

Module 16: Register an MLflow model in Azure Machine Learning Learn how to log and register an MLflow model in Azure Machine Learning. After completing this module, students will be able to:

• Log models with MLflow
• Understand the MLmodel format
• Register an MLflow model in Azure Machine Learning

Module 17: Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Explore model explanations, error analysis, counterfactuals, and causal analysis by creating a Responsible AI dashboard. You'll create and run the pipeline in Azure Machine Learning using the Python SDK v2 to generate the dashboard. After completing this module, students will be able to:

• Understand Azure Machine Learning's built-in components for responsible AI
• Create a Responsible AI dashboard
• Explore a Responsible AI dashboard

Module 18: Deploy a model to a managed online endpoint  Learn how to deploy models to a managed online endpoint for real-time inferencing. After completing this module, students will be able to:

• Use managed online endpoints
• Deploy your MLflow model to a managed online endpoint
• Deploy a custom model to a managed online endpoint
• Test online endpoints

Module 19: Deploy a model to a batch endpoint  Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you'll trigger a batch scoring job. After completing this module, students will be able to:

• Create a batch endpoint
• Deploy your MLflow model to a batch endpoint
• Deploy a custom model to a batch endpoint
• Invoke batch endpoints

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