You will learn how to operate machine learning solutions at cloud scale using Azure Machine Learning.
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.
Day 1 • Getting Started with Azure Machine Learning • Introduction to Azure Machine Learning • Working with Azure Machine Learning • Lab : Create an Azure Machine Learning Workspace • No-Code Machine Learning • Automated Machine Learning • Azure Machine Learning Designer • Lab : Use Automated Machine Learning • Lab : Use Azure Machine Learning Designer • Running Experiments and Training Models • Introduction to Experiments • Training and Registering Models • Lab : Run Experiments • Lab : Train Models
• Working with Data • Create and use datastores • Create and use datasets • Lab : Work with Compute • Working with Compute • Working with Environments • Working with Compute Targets • Lab : Work with Compute • Orchestrating Operations with Pipelines • Introduction to Pipelines • Publishing and Running Pipelines • Lab : Create a Pipeline
• Deploying and Consuming Models • Real-time Inferencing • Batch Inferencing • Continuous Integration and Delivery • Lab : Create a Real-time Inferencing Service • Lab : Create a Batch Inferencing Service • Training Optimal Models • Hyperparameter Tuning • Automated Machine Learning • Lab : Tune Hyperparameters • Lab : Use Automated Machine Learning from the SDK • Responsible Machine Learning • Differential Privacy • Model Interpretability • Fairness • Lab : Explore Differential provacy • Lab : Interpret Models • Lab : Detect and Mitigate Unfairness • Monitoring Models • Monitoring Models with Application Insights • Monitoring Data Drift • Lab : Monitor a Model with Application Insights • Lab : Monitor Data Drift