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Designing Machine/Deep Learning Models Using Azure CLI
Gain insights into Azure basics, build ML/deep learning pipelines, manage deployments, and analyze models ethically with responsible AI, ensuring fairness and bias mitigation.
34 Lessons
3 Projects
10h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- A working knowledge of Azure CLI for machine/deep learning
- Hands-on experience building and scheduling machine/deep learning pipelines
- The ability to deploy deep learning models using distributed training
- An understanding of MLflow integration with Azure Machine Learning
- A working knowledge of responsible AI using Azure Machine Learning
Learning Roadmap
1.
Introduction
Introduction
Learn how to use Azure's services, CLI tools, and comprehensive machine learning capabilities.
2.
Getting Started
Getting Started
Look at setting up Microsoft and Azure accounts and using Azure CLI commands.
3.
Creating Azure Resources
Creating Azure Resources
8 Lessons
8 Lessons
Work your way through creating Azure resources, including YAML, resource groups, VMs, and storage.
4.
Building Azure ML Pipeline
Building Azure ML Pipeline
6 Lessons
6 Lessons
Grasp the fundamentals of creating Azure ML pipelines, managing datasets, and optimizing models.
5.
Service Deployment
Service Deployment
6 Lessons
6 Lessons
Take a closer look at deploying, testing, and managing machine learning models in Azure.
6.
Creating a Deep Learning Pipeline
Creating a Deep Learning Pipeline
5 Lessons
5 Lessons
Follow the process of creating and deploying efficient and equitable deep learning models on Azure.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
Soudamini Sreepada
Principal Data Scientist, Mentor, Instructor with 18 years of experience with Microsoft R & D, India. Part of shipping multiple products like Data Protection Manager, windows-7, Bing from inception. Shipped multiple ML/DL products at scale.
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