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 YAML?
**YAML** stands for **YAML ain't markup language**. It is a user-friendly serialization language (similar to XML and JSON). YAML appears to be Gen-Y for XML. Don’t be mistaken; there is also ZAML, which is unrelated to YAML.
We can represent sequential, hierarchical data very well in YAML. It has steadily increased its popularity in recent years.

 YAML has the following key features:

- YAML is a broad language and can work with all programming languages. 
- It is mainly used for preparing configuration files.
- Its object serialization capabilities make it similar to the JSON format.
- YAML is a more readable language.

Azure supports the YAML language for pipeline creation. Azure Pipelines is a cloud service used for automatically building and testing code or ML projects. 

Using YAML templates, we can define reusable content, logic, and parameters in separate templates that load into the pipeline at runtime. 

We can divide these templates into one of  four categories:

- **Stage**: Define a set of stages for related jobs.

- **Job**: Define a collection of steps run by an agent.

- **Step**: Define a linear sequence of operations for a job.

- **Variable**: An alternative to hardcoded values or variable groups.

# YAML examples 

Let's look at a sample Azure job in YAML below:


Get introduced to YAML, a markup language used for building Azure ML pipelines, and its syntax.

YAML For Azure ML

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

YAML For Azure ML