Train and deploy a machine learning model with Azure Machine Learning (DP-3007)

To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.

Module 1: 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.

Module 2 : 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.

Module 3: Work with environments in Azure Machine Learning

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

Module 4: 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.

Module 5: Track model training with MLflow in jobs

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

Module 6: Register an MLflow model in Azure Machine Learning

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

Module 7: Deploy a model to a managed online endpoint

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

• Machine Learning Professionals
• Machine Learning Engineers