Module 1 : Design a data ingestion strategy for machine learning projects
- Identify your data source and format
- Choose how to serve data to machine learning workflows
- Design a data ingestion solution
Module 2 : Design a machine learning model training solution
- Identify your data source and format
- Choose how to serve data to machine learning workflows
- Design a data ingestion solution
Module 3 : Design a model deployment solution
- Understand how a model will be consumed.
- Decide whether to deploy your model to a real-time or batch endpoint.
Module 4 : Explore Azure Machine Learning workspace resources and assets
- Create an Azure Machine Learning workspace.
- Identify resources and assets.
- Train models in the workspace.
Module 5 : Explore developer tools for workspace interaction
- The Azure Machine Learning studio.
- The Python Software Development Kit (SDK).
- The Azure Command Line Interface (CLI).
Module 6 : Make data available in Azure Machine Learning
- Work with Uniform Resource Identifiers (URIs).
- Create and use datastores.
- Create and use data assets.
Module 7 : Work with compute targets in Azure Machine Learning
- Choose the appropriate compute target.
- Create and use a compute instance.
- Create and use a compute cluster.
Module 8 : Work with environments in Azure Machine Learning
- Understand environments in Azure Machine Learning.
- Explore and use curated environments.
- Create and use custom environments.
Module 9 : Find the best classification model with Automated Machine Learning
- Prepare your data to use AutoML for classification.
- Configure and run an AutoML experiment.
- Evaluate and compare models..
Module 10 : Track model training in Jupyter notebooks with MLflow
- Configure to use MLflow in notebooks
- Use MLflow for model tracking in notebooks
Module 11 : 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.
Module 12 : 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.
Module 13 : Run pipelines in Azure Machine Learning
- Create components.
- Build an Azure Machine Learning pipeline.
- Run an Azure Machine Learning pipeline.
Module 14 : Perform hyperparameter tuning with Azure Machine Learning
- Define a hyperparameter search space.
- Configure hyperparameter sampling.
- Select an early-termination policy.
- Run a sweep job.
Module 15 : 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.
Module 16 : Deploy a model to a batch endpoint
- Create a batch endpoint.
- Deploy your MLflow model to a batch endpoint.
- Deploy a custom model to a batch endpoint.
- Invoke batch endpoints.