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 deploy a machine learning model.
Prerequisites
None
Outline
- Introduction
- Understand URIs
- Create a datastore
- Create a data asset
- Exercise - Make data available in Azure Machine Learning
- Knowledge check
- Summary
- Introduction
- Choose the appropriate compute target
- Create and use a compute instance
- Create and use a compute cluster
- Exercise - Work with compute resources
- Knowledge check
- Summary
- Introduction
- Understand environments
- Explore and use curated environments
- Create and use custom environments
- Exercise - Work with environments
- Knowledge check
- Summary
- Introduction
- Convert a notebook to a script
- Run a script as a command job5
- Use parameters in a command job
- Exercise - Run a training script as a command job
- Knowledge check
- Summary
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
- Exercise - Use MLflow to track training jobs
- Knowledge check
- Summary
- Introduction
- Log models with MLflow
- Understand the MLflow model format
- Register an MLflow model
- Exercise - Log and register models with MLflow
- Knowledge check
- Summary
- Introduction
- Explore managed online endpoints
- Deploy your MLflow model to a managed online endpoint
- Deploy a model to a managed online endpoint
- Test managed online endpoints
- Exercise - Deploy an MLflow model to an online endpoint
- Knowledge check
- Summary
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