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 course , you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.
Additionally, you'll also learn to perform post-migration tasks like disaster recovery and monitoring for Azure SQL Database. These skills are essential for ensuring a smooth, efficient transition to Azure SQL Database, and maintaining its operation post-migration.
Level: Intermediate
Product: Azure, Azure Machine Learning
Audience/Role: AI Engineer, Data Engineer, Developer, Data Scientist
Subject: Machine Learning
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.
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.
Work with environments in Azure Machine Learning
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
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.
Track model training with MLflow in jobs
Learn how to track model training with MLflow in jobs when running scripts.
Register an MLflow model in Azure Machine Learning
Learn how to log and register an MLflow model in Azure Machine Learning.
Deploy a model to a managed online endpoint
Learn how to deploy models to a managed online endpoint for real-time inferencing.
This one-day, instructor-led training is recommended as preparation for the assessment Train and deploy a machine learning model with Azure Machine Learning
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