Module 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.
Design a machine learning model training solution.
Design a model deployment solution.
Design a machine learning operation solution.

Module 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. This learning path helps prepare you for Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

Explore Azure Machine Learning workspace resources and assets.
Explore developer tools for workspace interaction.
Make data available in Azure Machine Learning
Work with compute targets in Azure Machine Learning
Work with environments in Azure Machine Learning

Module 3: Work with data in Azure Machine Learning
Learn how to work with data in Azure Machine Learning. Whether you want to access data in notebooks or scripts, you can read data directly, through datastores, or data assets.

Make data available in Azure Machine Learning

Module 4: Experiment with Azure Machine Learning
Learn how to find the best model with automated machine learning (AutoML) and by experimenting in notebooks. This learning path helps prepare you for Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

Find the best classification model with Automated MachineLearning
Track model training in Jupyter notebooks with MLflow.

Module 5: Use notebooks for experimentation in Azure Machine Learning
Learn how to use Azure Machine Learning notebooks for experimentation. Similar to Jupyter, the notebooks are ideal for exploring your data and developing a machine learning model.

Track model training in Jupyter notebooks with MLflow.

Module 6: Train models with scripts in Azure Machine Learning
To prepare your machine learning workloads for production, you'll work with scripts. Learn how to train models with scripts in Azure Machine Learning. This learning path helps prepare you for Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

Run a training script as a command job in Azure MachineLearning
Track model training with MLflow in jobs.
Perform hyperparameter tuning with Azure Machine Learning

Module 7: 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 MachineLearning
Track model training with MLflow in jobs.
Perform hyperparameter tuning with Azure Machine Learning
Run pipelines in Azure Machine Learning

Module 8: 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
Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

Module 9: 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. This learning path helps prepare you for Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

Deploy a model to a managed online endpoint.
Deploy a model to a batch endpoint.

DP-100T01: Designing And Implementing A Data Science Solution On Azure