About this course
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.
Audience profile
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.
Before attending this course, students must have:
Module 1: Introduction to Azure Machine Learning
Lab: Creating an Azure Machine Learning Workspace
Lab: Working with Azure Machine Learning Tools
Module 2: No-Code Machine Learning with Designer
Lab: Creating a Training Pipeline with the Azure ML Designer
Lab: Deploying a Service with the Azure ML Designer
Module 3: Running Experiments and Training Models
Lab: Running Experiments
Lab: Training and Registering Models
Module 4: Working with Data
Lab: Working with Datastores
Lab: Working with Datasets
Module 5: Compute Contexts
Lab: Working with Environments
Lab: Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
Lab: Creating a Pipeline
Lab: Publishing a Pipeline
Module 7: Deploying and Consuming Models
Lab: Creating a Real-time Inferencing Service
Lab: Creating a Batch Inferencing Service
Module 8: Training Optimal Models
Lab: Tuning Hyperparameters
Lab: Using Automated Machine Learning
Module 9: Interpreting Models
Lab: Reviewing Automated Machine Learning Explanations
Lab: Interpreting Models
Module 10: Monitoring Models
Lab: Monitoring a Model with Application Insights
Lab: Monitoring Data Drift
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