Train and Deploy a Machine Learning Model with Azure Machine Learning
Machine Learning Professionals Machine Learning Engineers
Description
- Make data available in Azure Machine Learning
- Work with compute targets in Azure Machine Learning
- Work with environments in Azure Machine Learning
- Run a training script as a command job in Azure Machine Learning
- Track model training with MLflow in jobs
- Register an MLflow model in Azure Machine Learning
- Deploy a model to a managed online endpoint
Course Outline
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.