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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 in Microsoft Azure.
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.
If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.
Module 1: Getting Started with Azure Machine Learning
Lab : Create an Azure Machine Learning Workspace
Module 2: Visual Tools for Machine Learning
Lab : Use Automated Machine Learning
Lab : Use Azure Machine Learning Designer
Module 3: Running Experiments and Training Models
Lab : Train Models
Lab : Run Experiments
Module 4: Working with Data
Lab : Work with Data
Module 5: Working with Compute
Lab : Work with Compute
Module 6: Orchestrating Operations with Pipelines
Lab : Create a Pipeline
Module 7: Deploying and Consuming Models
Lab : Create a Real-time Inferencing Service
Lab : Create a Batch Inferencing Service
Module 8: Training Optimal Models
Lab : Use Automated Machine Learning from the SDK
Lab : Tune Hyperparameters
Module 9: Responsible Machine Learning
Lab : Explore Differential provacy
Lab : Interpret Models
Lab : Detect and Mitigate Unfairness
Module 10: Monitoring Models
Lab : Monitor Data Drift
Lab : Monitor a Model with Application Insights