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

# Why Azure virtual machines?
VMs give us the option to run applications without actually requiring us to buy the actual hardware.
We might need virtual servers mainly for the following reasons:

- Deploying the applications.

- Developing and testing the solutions.

 If we want to buy virtual servers for a limited duration, Azure virtual servers are the best! However, we still have to manage activities like configuration and patching the VMs. 
Azure offers a vast range of servers, including various operating systems and storage capabilities. Azure Virtual Machines regions are also widespread globally, so we should choose the location closest to us.

# Azure VM configurations

We need to consider several factors before creating the VM. 
Size is the first important consideration.
 
## Selecting the right size

- **General purpose:** Meant for general-purpose computing. For medium storage requirements and supports low–medium web traffic. D-series servers will come in this category. 

- **Compute optimized:** High CPU servers. Suitable for medium web traffic and batch processing. F-series servers will come under this category.

- **Memory-optimized:** High memory servers. Great for high storage and large cache requirements. E-series servers will fall under this category.

- **Storage optimized:** Optimized for high disk throughput. Ideal for big data and data warehousing applications. 
Lsv2 servers fall under this category.

- **GPU:** Ideal for heavy graphic processing and deep learning.
Choose N-series servers for this requirement.

- **High-performance compute:** Fastest and most powerful servers. H-series servers are available under this category.

Learn about the different configuration options for Azure virtual machines and how to create them.

Creating a Virtual Machine

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 Virtual Machine

Why Azure virtual machines?

Azure VM configurations

Selecting the right size