Train and manage a machine learning model with Azure Machine Learning

Subjects

1: Make data available in Azure Machine Learning

• Access data by using Uniform Resource Identifiers (URIs).
• Connect to cloud data sources with datastores.
• Use data asset to access specific files or folders.

2: Work with compute targets in Azure Machine Learning

• Choose the appropriate compute target.
• Work with compute instances and clusters.
• Manage installed packages with environments.

3: Run a training script as a command job in Azure Machine Learning

• Convert a notebook to a script.
• Test scripts in a terminal.
• Run a script as a command job.
• Use parameters in a command job.

4: Track model training with MLflow in jobs

• Use MLflow when you run a script as a job.
• Review metrics, parameters, artifacts, and models from a run.

5: Register an MLflow model in Azure Machine Learning

• Log models with MLflow.
• Understand the MLmodel format.
• Register an MLflow model in Azure Machine Learning.

6: Deploy a model to a managed online endpoint

• Use managed online endpoints.
• Deploy your MLflow model to a managed online endpoint.
• Deploy a custom model to a managed online endpoint.
• Test online endpoints.