Gain insights into Azure basics, build ML/deep learning pipelines, manage deployments, and analyze models ethically with responsible AI, ensuring fairness and bias mitigation.

“Build, Deploy MachineDeep Learning Models using Azure CLI”.png

azure.tar.gz

UserName

Location

ResourceGroup

Workspace

TenantId

AppId

Password

SubscriptionId

Compute

StorageKey

StorageAccount

OnlineEndpoint

BatchEndpoint

AppId_US

Password_US

RG1_US

WS1_US

Compute_US

scoring_uri

api_key

VirtualMachine

run_id

PrivateWorkspaceCreation

MLExtension

DefaultWorkspace

ResourceGroupCreation

ServicePrincipal

VMCreation

ComputeCreate

ResourceGroup Update

ComputeDelete

Demo-run-command-copy

Endpoint-Get Credentials

ComputeCreate-yml

EnvCreate-Docker

EnvCreate

EnvList

DataCreate-Simple

Storage Account Creation

Storage Account ContainerCreation

Storage Blob Upload

Storage Blob Download

Storage Blob Delete

JobCreate

Job-run-SPA

DataCreate-yml

VMList

Model-Create-SPA

Component-Create-SPA

endpoint-Create-SPA

ComputeDelete-copy

endpoint-uai-create-SPA

endpoint-batch-create-SPA

deployment-batch-create-SPA

deployment-online-create-SPA

endpoint-local-Create-SPA

RunCommand

VM-Create-SPA

PrivateWorkspaceCreation-copy

endpoint-Create-Local-SPA

deployment-batch-invoke

Run-command-SPA

In this course, you will start your journey by gaining a comprehensive understanding of the basics of Azure, including creating and managing Azure resources, and setting up the Azure CLI environment. 

Next, you will learn how to build Azure Machine Learning pipelines from scratch. Then, you’ll delve into deep learning and distributed deep learning pipelines. You’ll learn how to manage the deployment and scheduling of these models. You’ll also cover the complete model management strategies. Finally, the course will cover model analysis using responsible AI, teaching how to identify and mitigate potential biases in models and ensuring that models are ethical and fair. You’ll also learn how to analyze the models and identify potential areas for improvement.

By the end of this course, you’ll have a comprehensive understanding of Azure Machine Learning, including how to build complex pipelines, deploy models using online/batch methods, and manage and analyze models using tools like MLflow and responsible AI.

Designing Machine/Deep Learning Models Using Azure CLI

# What is a workspace in Azure?

An Azure Machine Learning workspace groups all the Azure Machine Learning resources and provides centralized control of assets and resources used in our ML workflows. We will discuss workspace creation using two important interfaces—Azure Machine Learning studio and Azure CLI. The workspace will be bound to a specific region (East US, West US, and so on).

# Workspace creation using the Azure Machine Learning portal

When we log in to the Azure space, there will be an option to create a workspace. Click the "+" button and provide the relevant details to create a workspace.

When we create a workspace, Azure creates the following resources internally:

- **Storage account**: Azure creates a default storage account that stores the ML experiment logs and reports. We can also add storage tables and blob containers in this space.

- **Azure Container Registry**: This registry is used to save the ML environments.

- **Azure Key Vault**: This is used to store secrets and other sensitive information needed by the workspace.

- **Application Insights**: Azure monitoring and diagnostic information.

Once we create the workspace, we can create and monitor ML experiments.

Learn about creating an Azure workspace from practical examples.

Creating Workspace

Creating a Workspace

Introduction

Getting Started

Creating Azure Resources

Building Azure ML Pipeline

Building a diabetes model pipeline

Service Deployment

Creating a Deep Learning Pipeline

Detect Posture Project

Conclusion

Appendix

Sentiment Analysis Using Natural Language Processing

Creating a Workspace

What is a workspace in Azure?

Workspace creation using the Azure Machine Learning portal