Designing and Implementing a Data Science Solution on Azure (DP-100T01)

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.