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

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ResourceGroup

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Storage Account Creation

Storage Account ContainerCreation

Storage Blob Upload

Storage Blob Download

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JobCreate

Job-run-SPA

DataCreate-yml

VMList

Model-Create-SPA

Component-Create-SPA

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deployment-batch-invoke

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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 an environment?

In the context of Azure, the **environment** is the software or package needed to run the machine learning job.
We can use the environments to set up and reproduce the same results for the ML jobs.
Azure Machine Learning comes with prebuilt environments, which can be used directly. To learn all the environments present in the resource group, we can use the command below:



We can create an environment using three different methods:
- Create an environment from an existing environment.
- Create an environment using an image.
- Create an environment using Docker.

## Creating an environment from an existing environment

We have seen the list of environments available in the previous command output. Let's use one of these environments. 
The `pytorch/pytorch:latest` contains the latest PyTorch environment.

We can use the command `az ml environment create -f env.xml`. Here, the `env.xml` file refers to the PyTorch environment.


Learn different ways to set up the machine learning software environment.

Creating an Environment

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 an Environment

What is an environment?