IA Designing and Implementing a Data Science Solution  Azure and Databricks

 Manage Azure resources for machine learning

  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

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Run experiments and train models

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