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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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 Storage?

In the previous lesson, we created the software environment required for ML solutions. We also need to address a critical aspect—storage. We will explore Azure Storage services in this section. Azure Storage provides highly available, scalable, and secure storage services using a diversified set of datastores.
## Advantages of using Azure Storage services:

- **High availability:** Redundancy and replication across multiple data centers ensures that the data is highly available.

- **Security:** Data is encrypted when stored at Azure Storage services. Azure also provides control over storage permissions.

- **Scalability:** Azure provides data scalability and manages the performance of highly scaled data.

- **Accessible:** Azure databases can be accessed using HTTP and HTTPS URLs. The storage services also support friendly SDKs in several languages.
 


# Types of Azure storage

- **Azure blobs:** These are text and binary data object stores. They are also helpful for unstructured and big data. Azure Data Lake Storage Gen2 provides big data analytics.

- **Azure files:** These are managed file shares that can be mounted by cloud and on-premises deployments. We can access them from Windows, Linux, or macOS.

- **Azure tables:** These are schema-less storage meant for nonrelational structured data.

- **Azure queues:** These are commonly used for asynchronous communication. They are used to store messages up to 64 KB.

- **Azure disks:** These are managed virtual disks similar to physical disks.

There are two built-in datastores in every workspace:
- Azure Storage Blob Container
- Azure Storage File Container 

They are used as system storage by Azure Machine Learning.



Learn about storage accounts and different types of storage mechanisms in Azure. 

Creating Storage

Learn about storage accounts and different types of storage mechanisms in Azure.

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 Storage

Why Azure Storage?

Advantages of using Azure Storage services:

Types of Azure storage