- 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.
- 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.
- Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.
In this module, you learn how to:
- Define a hyperparameter search space.
- Configure hyperparameter sampling.
- Select an early-termination policy.
- Run a sweep job.
Build with Azure OpenAI, Copilot Studio & Agentic Frameworks — Microsoft Certified
Foundations of Data Visualization - Self Paced Online
Overview
Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off
One annual plan covers every course and certificate on Coursera. 40% off for a limited time.
Get Full Access
Syllabus
- 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
- Module assessment
- Summary
- Track model training with MLflow in jobs
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
- Exercise - Use MLflow to track training jobs
- Module assessment
- Summary
- 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
- Module assessment
- Summary