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 are Azure command-line tools?

**Azure command-line tools** help manage most Azure resources and services-related tasks by allowing us to automate and scale our tasks in Azure. They can also be grouped into a bash file, making sharing easy. 

Azure mainly supports three types of command-line tools.
1. **Azure CLI:** This is an extension to the Windows command-line environment. The commands would be in Bash format.
2. **PowerShell:** This is an extension to the Windows PowerShell environment. The commands need to be in cmdlet format.
3. **Azure Cloud Shell**: This supports Bash and PowerShell environments via a web interface. There's no installation required.

# What is Azure CLI?

The **Azure Command-Line Interface (CLI)** is a simple, cross-platform command-line tool to connect to Azure. Using it, we can do the following: 
- Create and manage Azure resources (VMs/databases). 
- Execute administrative commands on Azure resources (workgroups, storage accounts, and so on).
- Leverage existing services (ML).

The tool is designed to run quickly with Azure, with faster development and automation.


We can launch a shell on Windows or Bash on Linux or macOS. We can bundle the CLI commands into a shell script to automate repetitive tasks. The latest version of Azure CLI v2 supports training, running, and deploying ML models using the Azure ml extension. Azure functionalities can also be achieved using the GUI-friendly Azure Studio and developer-friendly Azure SDK. 










**Azure Cloud Shell:**
We can access the Azure CLI from Azure Cloud Shell as well. It's convenient to start immediately without installing or requiring a separate desktop machine. Azure Cloud Shell includes an integrated file editor built from the open-source Monaco Editor.

Type shell.azure.com into your browser.

Alternatively, select the Cloud Shell icon from [the Azure portal](https://azure.microsoft.com/en-us/get-started/azure-portal). 

We can choose one of two options—Bash or PowerShell—from the shell options. If we select Bash, it launches the Bash console. We can switch it to PowerShell at any later point in time.

After selecting the suitable shell environment, we can choose the correct subscription (if we have multiple subscriptions) and select storage. 
That's all! We're good to go!
We can switch the command-line environment to Bash or PowerShell whenever we want.
 
**Cloud Shell Features**

- It runs a temporary session and runs for each user.
- If Cloud Shell is idle for 20 minutes, it will timeout to optimize the resources. It is also meant to work in interactive mode; therefore, any long-running processes are timed out. 
- We have to mount a file share in the shell environment.
- We will have to use the same file share for both the Bash and PowerShell environments.
- A 5-GB image held in the file share is reserved for `$HOME`.
- User permissions would be the same as any Linux user.



**PowerShell**:
Azure PowerShell offers a set of cmdlets for managing Azure resources. The syntax is similar to Windows PowerShell. It provides solid features for automating repetitive tasks.

The Az PowerShell module works with PowerShell 7.0.6 LTS and PowerShell 7.1.3 or higher on all platforms, including Windows, macOS, and Linux. It can also be installed locally on these operating systems. It's also compatible with Windows PowerShell 5.1.

# Which command-line tool to use

Azure CLI is a developer-friendly environment that relieves us from setting up all the required environments step-by-step (unlike Azure SDK). It also gives us greater freedom, customization, and faster development (better than Azure Studio). We can also automate and schedule jobs using Azure CLI. Once we install Azure CLI and launch the command prompt, we're good to go.


Azure CLI and Azure PowerShell are cross-platform command-line tools and offer similar functionalities.

The Azure CLI syntax is similar to that of Bash scripting. Azure CLI works the same way as the Linux scripting system. Azure CLI commands can be executed from the PowerShell environment as well. Azure CLI outputs in JSON, YAML, and various output formats.

Azure PowerShell is a seamless fit for developers with Windows backgrounds. PowerShell cmdlets follow a verb-noun naming scheme, and the output is returned as objects.

Azure Cloud Shell doesn't need to be in sync with a recent version of Azure PowerShell and Azure CLI because the publishing schedule for all three tools is different. Azure Cloud Shell is more suitable for learning and intended for small-scale jobs. It's not recommended for enterprise-scale applications.

This course will set up the Azure environment, create ML models, and deploy them as a service—using the Azure CLI for everything.

After selecting the suitable shell environment, we can choose the correct subscription (if we have multiple subscriptions) and select storage. 
That's all! We're good to go!
We can switch the command-line environment to Bash or PowerShell whenever we want.
 
**Cloud Shell Features**

- It runs a temporary session and runs for each user.
- If Cloud Shell is idle for 20 minutes, it will timeout to optimize the resources. It is also meant to work in interactive mode; therefore, any long-running processes are timed out. 
- We have to mount a file share in the shell environment.
- We will have to use the same file share for both the Bash and PowerShell environments.
- A 5-GB image held in the file share is reserved for `＄HOME`.
- User permissions would be the same as any Linux user.



Get introduced to various command interface tools that Azure supports—Azure CLI, Azure PowerShell, and Azure Cloudshell—and their usage.

Introduction to Azure CLI

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

Introduction to Azure CLI