IA Designing and Implementing a Data Sci | firstconsulting-1

• 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

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