Information Technology
DP-3007 Train and deploy a machine learning model with Azure Machine Learning
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.
1 Day
8 Hrs
7 Lessons
Agenda
- Understand URIs
- Create a datastore
- Create a data asset
- Choose the appropriate compute target
- Create and use a compute instance
- Create and use a compute cluster
- Understand environments
- Explore and use curated environments
- Create and use custom environments
- Convert a notebook to a script
- Run a script as a command job
- Use parameters in a command job
- Track metrics with MLflow
- View metrics and evaluate models
- Log models with MLflow
- Understand the MLflow model format
- Register an MLflow model
- 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
FREE
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Course Type: Instructor Led