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Set up an Azure Machine Learning workspace (30-35%)
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 objects in an Azure Machine Learning workspace
� register and maintain data stores
� create and manage datasets
Manage experiment compute contexts
� create a compute instance
� determine appropriate compute specifications for a training workload
� create compute targets for experiments and training

Run experiments and train models (25-30%)
Create models by using Azure Machine Learning Designer
� create a training pipeline by using Designer
� ingest data in a Designer pipeline
� use Designer modules to define a pipeline data flow
� use custom code modules in Designer
Run training scripts in an Azure Machine Learning workspace
� create and run an experiment by using the Azure Machine Learning SDK
� consume data from a data store in an experiment by using the Azure Machine Learning
SDK
� consume data from a dataset in an experiment by using the Azure Machine Learning
SDK
� choose an estimator
Generate metrics from an experiment run
� log metrics from an experiment run
� retrieve and view experiment outputs
� use logs to troubleshoot experiment run errors
Automate the model training process
� create a pipeline by using the SDK
� pass data between steps in a pipeline
� run a pipeline
� monitor pipeline runs

Optimize and manage models (20-25%)
Use Automated ML to create optimal models
� use the Automated ML interface in Studio
� use Automated ML from the Azure ML SDK
� select scaling functions and pre-processing options
� determine algorithms to be searched
� define a primary metric
� get data for an Automated ML run
� retrieve the best model
Use Hyperdrive to rune hyperparameters
� select a sampling method
� define the search space
� define the primary metric
� define early termination options
� find the model that has optimal hyperparameter values
Use model explainers to interpret models
� select a model interpreter
� generate feature importance data
Manage models
� register a trained model
� monitor model history
� monitor data drift

Deploy and consume models (20-25%)
Create production compute targets
� consider security for deployed services
� evaluate compute options for deployment
Deploy a model as a service
� configure deployment settings
� consume a deployed service
� troubleshoot deployment container issues
Create a pipeline for batch inferencing
� publish a batch inferencing pipeline
� run a batch inferencing pipeline and obtain outputs
Publish a Designer pipeline as a web service
� create a target compute resource
� configure an Inference pipeline
� consume a deployed endpoint

Set up an Azure Machine Learning workspace (30-35%)
Create an Azure Machine Learning workspace
� create an Azure Machine Learning workspace
� configure workspace settings
� manage a workspace by using Azure Machine Learning sStudio
Manage data objects in an Azure Machine Learning workspace
� register and maintain data stores
� create and manage datasets
Manage experiment compute contexts
� create a compute instance
� determine appropriate compute specifications for a training workload
� create compute targets for experiments and training

Run experiments and train models (25-30%)
Create models by using Azure Machine Learning Designer
� create a training pipeline by using Azure Machine Learning Ddesigner
� ingest data in a Designer designer pipeline
� use Designer designer modules to define a pipeline data flow
� use custom code modules in Designer designer
Run training scripts in an Azure Machine Learning workspace
� create and run an experiment by using the Azure Machine Learning SDK
� consume data from a data store in an experiment by using the Azure Machine Learning
SDK
� consume data from a dataset in an experiment by using the Azure Machine Learning
SDK
� choose an estimator for a training experiment
Generate metrics from an experiment run
� log metrics from an experiment run
� retrieve and view experiment outputs
� use logs to troubleshoot experiment run errors
Automate the model training process
� create a pipeline by using the SDK
� pass data between steps in a pipeline
� run a pipeline
� monitor pipeline runs

Optimize and manage models (20-25%)
Use Automated ML to create optimal models
� use the Automated ML interface in Azure Machine Learning Studiostudio
� use Automated ML from the Azure Machine Learning SDK
� select scaling functions and pre-processing options
� determine algorithms to be searched
� define a primary metric
� get data for an Automated ML run
� retrieve the best model
Use Hyperdrive to rune tune hyperparameters
� select a sampling method
� define the search space
� define the primary metric
� define early termination options
� find the model that has optimal hyperparameter values
Use model explainers to interpret models
� select a model interpreter
� generate feature importance data
Manage models
� register a trained model
� monitor model history
� monitor data drift

Deploy and consume models (20-25%)
Create production compute targets
� consider security for deployed services
� evaluate compute options for deployment
Deploy a model as a service
� configure deployment settings
� consume a deployed service
� troubleshoot deployment container issues
Create a pipeline for batch inferencing
� publish a batch inferencing pipeline
� run a batch inferencing pipeline and obtain outputs
Publish a Designer designer pipeline as a web service
� create a target compute resource
� configure an Inference pipeline
� consume a deployed endpoint