- Manage Azure resources for machine learning (25-30% )
- Run experiments and train models (20-25% )
- Deploy and operationalize machine learning solutions (35-40%)
- Implement responsible machine learning (5-10%)
Manage Azure resources for machine learning (25-30%)
Create an Azure Machine Learning workspace
- create an Azure Machine Learning workspace
- configure workspace settings
- manage a workspace by using Azure Machine Learning studio
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
Implement security and access control in Azure Machine Learning
- determine access requirements and map requirements to built-in roles
- create custom roles
- manage role membership
- manage credentials by using Azure Key Vault
Set up an Azure Machine Learning development environment
- create compute instances
- share compute instances
- access Azure Machine Learning workspaces from other development environments
Set up an Azure Databricks workspace
- create an Azure Databricks workspace
- create an Azure Databricks cluster
- create and run notebooks in Azure Databricks
- link and Azure Databricks workspace to an Azure Machine Learning workspace
Run experiments and train models (20-25%)
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
Generate metrics from an experiment run
- log metrics from an experiment run
- retrieve and view experiment outputs
- use logs to troubleshoot experiment run errors
- use MLflow to track experiments
- track experiments running in Azure Databricks
Use Automated Machine Learning to create optimal models
- use the Automated ML interface in Azure Machine Learning studio
- use Automated ML from the Azure Machine Learning SDK
- select pre-processing options
- select the algorithms to be searched
- define a primary metric
- get data for an Automated ML run
- retrieve the best model
Tune hyperparameters with Azure Machine Learning
- select a sampling method
- define the search space
- define the primary metric
- define early termination options
- find the model that has optimal hyperparameter values
Deploy and operationalize machine learning solutions (35-40%)
Select compute for model deployment
- consider security for deployed services
- evaluate compute options for deployment
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
Create an Azure Machine Learning pipeline for batch inferencing
- configure a ParallelRunStep
- configure compute for a batch inferencing pipeline
- publish a batch inferencing pipeline
- run a batch inferencing pipeline and obtain outputs
- obtain outputs from a ParallelRunStep
Publish an Azure Machine Learning designer pipeline as a web service
- create a target compute resource
- configure an inference pipeline
- consume a deployed endpoint
Implement pipelines by using the Azure Machine Learning SDK
- create a pipeline
- pass data between steps in a pipeline
- run a pipeline
- monitor pipeline runs
Apply ML Ops practices
- trigger an Azure Machine Learning pipeline from Azure DevOps
- automate model retraining based on new data additions or data changes
- refactor notebooks into scripts
- implement source control for scripts
Implement responsible machine learning (5-10%)
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
Describe privacy considerations for data
- describe principles of differential privacy
- specify acceptable levels of noise in data and the effects on privacy
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