1.    Design a data ingestion strategy formachine learning projects

Learn how to design a dataingestion solution for training data used in machine learning projects.

Learning objectives

In this module, you'lllearn how to:

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

2.    Design a machine learning modeltraining solution

Learn how to design a model training solution for machinelearning projects.

Learning objectives

Inthis module, you'll learn how to:

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

3.     Design a model deployment solution

Learn how to design a model deployment solution and how therequirements of the deployed model can affect the way you train a model.

Learning objectives

In this module, you'll learn how to:

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

4.    Explore Azure Machine Learningworkspace resources and assets

As a data scientist, you can use Azure Machine Learning totrain and manage your machine learning models. Learn what Azure MachineLearning is, and get familiar with all its resources and assets.

Learning objectives

In this module, you'll learn how to:

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

5.    Explore developer tools for workspaceinteraction

Learn how you can interact with the Azure Machine Learningworkspace. You can use the Azure Machine Learning studio, the Python SDK (v2),or the Azure CLI (v2).

Learning objectives

In this module, you'll learn how and when to use:

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

6.    Make data available in Azure MachineLearning

Learn about how to connect to data from the Azure MachineLearning workspace. You'll be introduced to datastores and data assets.

Learning objectives

In this module, you'll learn how to:

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

7.    Work with compute targets in AzureMachine Learning

Learn how to work with compute targets in Azure MachineLearning. Compute targets allow you to run your machine learning workloads. Explore how and when you canuse a compute instance or compute cluster.

Learning objectives

In this module, you'll learn how to:

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

8.    Work with environments in Azure MachineLearning

Learn how to use environments in Azure Machine Learning torun scripts on any compute target.

Learning objectives

In this module, you'll learn how to:

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

9.     Find the best classification model withAutomated Machine Learning

Learn how to find the best classification model withautomated machine learning (AutoML). You'll use the Python SDK (v2) toconfigure and run an AutoML job.

Learning objectives

In this module, you'll learn how to:

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

10. Track model training in Jupyternotebooks with MLflow

Learn how to use MLflow for model tracking whenexperimenting in notebooks.

Learning objectives

In this module, you'll learn how to:

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

11. Run a training script as a command jobin Azure Machine Learning

Learn how to convert your code to a script and run it as acommand job in Azure Machine Learning.

Learning objectives

In this module, you'll learn how 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.

12. Track model training with MLflow injobs

Learn how to track model training with MLflow in jobs whenrunning scripts.

Learning objectives

In this module, you learn how to:

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

13. Run pipelines in Azure Machine Learning

Learn how to create and use components to build pipeline inAzure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.

Learning objectives

In this module, you'll learn how to:

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

14. Perform hyperparameter tuning withAzure Machine Learning

Learn how to perform hyperparameter tuning with a sweep jobin Azure Machine Learning.

Learning objectives

In this module, you'll learn how to:

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

15. Deploy a model to a managed onlineendpoint

Learn how to deploy models to a managed online endpoint forreal-time inferencing.

Learning objectives

In this module, you'll learn how 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.

16. Deploy a model to a batch endpoint

Learn how to deploy models to a batch endpoint. When youinvoke a batch endpoint, you'll trigger a batch scoring job.

Learning objectives

In this module, you'll learn how to:

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