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.
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.
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
Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques. Specifically:
To gain these prerequisite skills, take the following free online training before attending the course:
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
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