PART 1: Design a machine learning solution
There are many options on Azure to train and consume machine learning models. Which service best fits your scenario can depend on a myriad of factors. Learn how to identify important requirements and when to use which service when you want to use machine learning models.

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.

Lessons
Identify your data source and format
Choose how to serve data to machine learning workflows
Design a data ingestion solution
Exercise: Design a data ingestion strategy

Design a machine learning model training solution
Learn how to design a model training solution for machine learning projects.

Lessons
Identify machine learning tasks
Choose a service to train a machine learning mode
Decide between compute options
Exercise: Design a model training strategy

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.

Lessons
Understand how model will be consumed
Decide on real-time or batch deployment
Exercise – Design a deployment solution

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.

Lessons
Explore an MLOps architecture
Design for monitoring
Design for retraining

PART 2: Explore and configure the Azure Machine Learning workspace
Throughout this learning path you explore and configure the Azure Machine Learning workspace. Learn how you can create a workspace and what you can do with it. Explore the various developer tools you can use to interact with the workspace. Configure the workspace for machine learning workloads by creating data assets and compute resources.

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.

Lessons
Create an Azure Machine Learning workspace
Identify Azure Machine Learning resources
Identify Azure Machine Learning assets
Train models in the workspace
Exercise – Explore the workspace

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).

Lessons
Explore the studio
Explore the Python SDK
Explore the CLI
Exercise – Explore the developer tools

Make data available in Azure Machine Learning
Learn about how to connect to data from the Azure Machine Learning workspace. You’re introduced to datastores and data assets.

Lessons
Understand URIs
Create a datastore
Create a data asset
Exercise – Make data available in Azure Machine Learning

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.

Lessons
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster
Exercise – Work with compute resources

Work with environments in Azure Machine Learning
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

Lessons
Understand environments
Explore and use curated environments
Create and use custom environments
Exercise – Work with environments

PART 3: Experiment with Azure Machine Learning
Learn how to find the best model with automated machine learning (AutoML) and by experimenting in notebooks.

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.

Lessons
Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models
Exercise – Find the best classification model with Automated Machine Learning

Track model training in Jupyter notebooks with MLflow
Learn how to use MLflow for model tracking when experimenting in notebooks.

Lessons
Configure MLflow for model tracking in notebooks
Train and track models in notebooks
Exercise – Track model training

PART 4: Optimize model training with Azure Machine Learning
Learn how to optimize model training in Azure Machine Learning by using scripts, jobs, components and pipelines.

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.

Lessons
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job
Exercise – Run a training script as a command job

Track model training with MLflow in jobs
Learn how to track model training with MLflow in jobs when running scripts.

Lessons
Track metrics with MLflow
View metrics and evaluate models
Exercise – Use MLflow to track training jobs

Perform hyperparameter tuning with Azure Machine Learning
Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.

Lessons
Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning
Exercise – Run a sweep job

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.

Lessons
Create components
Create a pipeline
Run a pipeline job
Exercise – Run a pipeline job

PART 5: Manage and review models in Azure Machine Learning
Learn how to manage and review models in Azure Machine Learning by using MLflow to store your model files and using responsible AI features to evaluate your models.

Register an MLflow model in Azure Machine Learning
Learn how to log and register an MLflow model in Azure Machine Learning.

Lessons
Log models with MLflow
Understand the MLflow model format
Register an MLflow model
Exercise – Log and register models with MLflow

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.

Lessons
Understand Responsible AI
Create the Responsible AI dashboard
Evaluate the Responsible AI dashboard
Exercise – Explore the Responsible AI dashboard

PART 6: Deploy and consume models with Azure Machine Learning
Learn how to deploy a model to an endpoint. When you deploy a model, you can get real-time or batch predictions by calling the endpoint.

Deploy a model to a managed online endpoint
Learn how to deploy models to a managed online endpoint for real-time inferencing.

Lessons
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints
Exercise – Deploy an MLflow model to an online endpoint

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.

Lessons
Understand and create batch endpoints
Deploy your MLflow model to a batch endpoint
Deploy a custom model to a batch endpoint
Invoke and troubleshoot batch endpoints
Exercise – Deploy an MLflow model to a batch endpoint