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Training
DP-3007: Train and manage a machine learning model with Azure Machine Learning
Course Information
Course Name
DP-3007: Train and manage a machine learning model with Azure Machine Learning
Duration
1 Day
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
Overview
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 manage a machine learning model.
Overview
Overview
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 manage a machine learning model.
Audience Profile
Prerequisities
At Course Completion
At Course Completion
In this module, you’ve learned how to:
- Access data by using URIs.
- Connect to cloud data sources with datastores.
- Use data asset to access specific files or folders.
Course Outline
Course Outline
Module 1: Make data available in Azure Machine Learning
Module 2: Work with compute targets in Azure Machine Learning
Module 3: Work with environments in Azure Machine Learning
Module 4: Run a training script as a command job in Azure Machine Learning
Module 5: Track model training with MLflow in jobs
Module 6: Register an MLflow model in Azure Machine Learning
Module 7: Deploy a model to a managed online endpoint
