- Module 1: Run a command job with the Azure Machine Learning Python SDK v2
In this module, you'll learn how to:
- Convert a notebook to a script.
- Test scripts in a terminal.
- Run a script as a command job.
- Use parameters in a command job.
- Module 2: Learn how to track model training with MLflow in jobs when running scripts.
In this module, you learn how to:
- Use MLflow when you run a script as a job.
- Review metrics, parameters, artifacts, and models from a run.
- Module 3: Azure Machine Learning Python SDK v2.
In this module, you'll learn how to:
- Define a hyperparameter search space.
- Configure hyperparameter sampling.
- Select an early-termination policy.
- Run a sweep job.
Train models with scripts in Azure Machine Learning
Overview
Syllabus
- Module 1: Module 1: Run a training script as a command job in Azure Machine Learning
- Introduction
- Convert a notebook to a script
- Run a script as a command job
- Use parameters in a command job
- Exercise - Run a training script as a command job
- Knowledge check
- Summary
- Module 2: Module 2: Track model training with MLflow in jobs
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
- Exercise - Use MLflow to track training jobs
- Knowledge check
- Summary
- Module 3: Module 3: Perform hyperparameter tuning with Azure Machine Learning
- Introduction
- Define a search space
- Configure a sampling method
- Configure early termination
- Use a sweep job for hyperparameter tuning
- Exercise - Run a sweep job
- Knowledge check
- Summary
Tags
Related Courses
-
Train and manage a machine learning model with Azure Machine Learning
-
Train models in Azure Machine Learning with the CLI (v2)
-
Manage and review models in Azure Machine Learning
-
Implement a machine learning solution with Azure Databricks
-
End-to-end machine learning operations (MLOps) with Azure Machine Learning
-
Build and operate machine learning solutions with Azure Machine Learning
