Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.

Full Time

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.

4 days

28 Hours

Module 1: Design a data ingestion strategy for machine learning projects

Module 2: Design a machine learning model training solution

Module 3: Design a model deployment solution

Module 4: Explore Azure Machine Learning workspace resources and assets

Module 5: Explore developer tools for workspace interaction

Module 6: Make data available in Azure Machine Learning

Module 7: Work with compute targets in Azure Machine Learning

Module 8: Work with environments in Azure Machine Learning

Module 9: Find the best classification model with Automated Machine Learning

Module 10: Track model training in Jupyter notebooks with MLflow

Module 11: Run a training script as a command job in Azure Machine Learning

Module 12: Track model training with MLflow in jobs

Module 13: Run pipelines in Azure Machine Learning

Module 14: Perform hyperparameter tuning with Azure Machine Learning

Module 15: Deploy a model to a managed online endpoint

Module 16: Deploy a model to a batch endpoint