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
In this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
- Working with Datastores and Datasets
Module 5: Working with Compute
One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.
- Working with Environments and Compute Targets
Module 6: Orchestrating Operations with Pipelines
Here you learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.
- Introduction to Pipelines
- Publishing and Running Pipelines
Module 7: Deploying and Consuming Models
Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
- Real-time Inferencing and Batch Inferencing
- Continuous Integration and Delivery
Module 8: Training Optimal Models
In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.
- Hyperparameter Tuning
- Automated Machine Learning
Module 9: Responsible Machine Learning
This module explores some considerations and techniques for applying responsible machine learning principles.
- Differential Privacy, Model Interpretability and Fairness
Module 10: Monitoring Models
After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.
- Monitoring Models with Application Insights and Data Drift