Designing and Implementing a Data Science Solution on Azure

• Identify your data source and format
• Choose how to serve data to machine learning workflows
• Design a data ingestion solution
• Identify machine learning tasks
• Choose a service to train a model
• Choose between compute options
• Understand how a model will be consumed.
• Decide whether to deploy your model to a real-time or batch endpoint.
• Explore an MLOps architecture.
• Design for monitoring.
• Design for retraining.
• Create an Azure Machine Learning workspace.
• Identify resources and assets.
• Train models in the workspace.
• The Azure Machine Learning studio.
• The Python Software Development Kit (SDK).
• The Azure Command Line Interface (CLI).
• Access data by using Uniform Resource Identifiers (URIs).
• Connect to cloud data sources with datastores.
• Use data asset to access specific files or folders.
• Choose the appropriate compute target.
• Work with compute instances and clusters.
• Manage installed packages with environments.
• Understand environments in Azure Machine Learning.
• Explore and use curated environments.
• Create and use custom environments.
• Prepare your data to use AutoML for classification.
• Configure and run an AutoML experiment.
• Evaluate and compare models.
• Configure to use MLflow in notebooks
• Use MLflow for model tracking in notebooks
• Convert a notebook to a script.
• Test scripts in a terminal.
• Run a script as a command job.
• Use parameters in a command job.
• Use MLflow when you run a script as a job.
• Review metrics, parameters, artifacts, and models from a run.
• Define a hyperparameter search space.
• Configure hyperparameter sampling.
• Select an early-termination policy.
• Run a sweep job.
• Create components.
• Build an Azure Machine Learning pipeline.
• Run an Azure Machine Learning pipeline.
• Log models with MLflow.
• Understand the MLmodel format.
• Register an MLflow model in Azure Machine Learning.
• Understand Azure Machine Learning’s built-in components for responsible AI.
• Create a Responsible AI dashboard.
• Explore a Responsible AI dashboard.
• 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.
• Create a batch endpoint.
• Deploy your MLflow model to a batch endpoint.
• Deploy a custom model to a batch endpoint.
• Invoke batch endpoints.

Please review the prerequisites listed for each module in the course content and click on the provided links for more information.

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

1. Design a data ingestion strategy for machine learning projects

Learn how to design a data ingestion solution for training data used in machine learning projects.

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2. Design a machine learning model training solution

Learn how to design a model training solution for machine learning projects.

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3. Design a model deployment solution

Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model.

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4. Design a machine learning operations solution

Learn about machine learning operations or MLOps to bring a model from development to production. Identify options for monitoring and retraining when preparing a model for production.

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5. Explore Azure Machine Learning workspace resources and assets

As a data scientist, you can use Azure Machine Learning to train and manage your machine learning models. Learn what Azure Machine Learning is, and get familiar with all its resources and assets.

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6. Explore developer tools for workspace interaction

Learn how you can interact with the Azure Machine Learning workspace. You can use the Azure Machine Learning studio, the Python SDK (v2), or the Azure CLI (v2).

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7. Make data available in Azure Machine Learning

Learn about how to connect to data from the Azure Machine Learning workspace. You’re introduced to datastores and data assets.

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8. Work with compute targets in Azure Machine Learning

Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.

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9. Work with environments in Azure Machine Learning

Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

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10. Find the best classification model with Automated Machine Learning

Learn how to find the best classification model with automated machine learning (AutoML). You’ll use the Python SDK (v2) to configure and run an AutoML job.

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11. Track model training in Jupyter notebooks with MLflow

Learn how to use MLflow for model tracking when experimenting in notebooks.

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12. Run a training script as a command job in Azure Machine Learning

Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.

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13. Track model training with MLflow in jobs

Learn how to track model training with MLflow in jobs when running scripts.

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14. Perform hyperparameter tuning with Azure Machine Learning

Learn how to perform hyperparameter tuning with a sweep job in Azure Machine Learning.

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15. Run pipelines in Azure Machine Learning

Learn how to create and use components to build pipeline in Azure Machine Learning. Run and schedule Azure Machine Learning pipelines to automate machine learning workflows.

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16. Register an MLflow model in Azure Machine Learning

Learn how to log and register an MLflow model in Azure Machine Learning.

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17. Create and explore the Responsible AI dashboard for a model in Azure Machine Learning

Explore model explanations, error analysis, counterfactuals, and causal analysis by creating a Responsible AI dashboard. You’ll create and run the pipeline in Azure Machine Learning using the Python SDK v2 to generate the dashboard.

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18. Deploy a model to a managed online endpoint

Learn how to deploy models to a managed online endpoint for real-time inferencing.

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19. Deploy a model to a batch endpoint

Learn how to deploy models to a batch endpoint. When you invoke a batch endpoint, you’ll trigger a batch scoring job.

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