• Manage Azure resources for machine learning
• Run experiments and train models
• Deploy and operationalize machine learning solutions
• Implement responsible machine learning

Course Outline

Manage data in an Azure Machine Learning workspace

• select Azure storage resources
• register and maintain datastores
• create and manage datasets

Manage compute for experiments in Azure Machine Learning

• determine the appropriate compute specifications for a training workload
• create compute targets for experiments and training
• configure Attached Compute resources including Azure Databricks
• monitor compute utilization

Create models by using the Azure Machine Learning designer

• create a training pipeline by using Azure Machine Learning designer
• ingest data in a designer pipeline
• use designer modules to define a pipeline data flow
• use custom code modules in designer

Run model training scripts

• create and run an experiment by using the Azure Machine Learning SDK
• configure run settings for a script
• consume data from a dataset in an experiment by using the Azure Machine Learning SDK
• run a training script on Azure Databricks compute
• run code to train a model in an Azure Databricks notebook

Deploy a model as a service

• configure deployment settings
• deploy a registered model
• deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint
• consume a deployed service
• troubleshoot deployment container issues

Manage models in Azure Machine Learning

• register a trained model
• monitor model usage
• monitor data drift

Use model explainers to interpret models

• select a model interpreter
• generate feature importance data

Describe fairness considerations for models

• evaluate model fairness based on prediction disparity
•  mitigate model unfairness

About The Exam