Designing and Implementing a Data Science Solution on Azure
Description
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.
Intended audience
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.
Topics
Module 1: Getting Started with Azure Machine Learning
Introduction to Azure Machine Learning
Working with Azure Machine Learning
Module 2: Visual Tools for Machine Learning
Automated Machine Learning
Azure Machine Learning Designer
Module 3: Running Experiments and Training Models
Introduction to Experiments
Training and Registering Models
Module 4: Working with Data
Working with Datastores
Working with Datasets
Module 5: Working with Compute
Working with Environments
Working with Compute Targets
Module 6: Orchestrating Operations with Pipelines
Introduction to Pipelines
Publishing and Running Pipelines
Module 7: Deploying and Consuming Models
Real-time Inferencing
Batch Inferencing
Continuous Integration and Delivery
Module 8: Training Optimal Models
Hyperparameter Tuning
Automated Machine Learning
Module 9: Responsible Machine Learning
Differential Privacy
Model Interpretability
Fairness
Module 10: Monitoring Models
Monitoring Models with Application Insights
Monitoring Data Drift
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