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Azure Machine Learning Engineering

You're reading from Azure Machine Learning Engineering Deploy, fine-tune, and optimize ML models using Microsoft Azure

Product type Paperback

Published in Jan 2023

Publisher Packt

ISBN-13 9781803239309

Length 362 pages

Edition 1st Edition

Tools

Azure

Concepts

Machine Learning

Authors (4):

Balamurugan Balakreshnan

Dennis Michael Sawyers

Sina Fakhraee Ph.D

Megan Masanz

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: Training and Tuning Models with the Azure Machine Learning Service

2. Chapter 1: Introducing the Azure Machine Learning Service FREE CHAPTER

3. Chapter 2: Working with Data in AMLS

4. Chapter 3: Training Machine Learning Models in AMLS

5. Chapter 4: Tuning Your Models with AMLS

6. Chapter 5: Azure Automated Machine Learning

7. Part 2: Deploying and Explaining Models in AMLS

8. Chapter 6: Deploying ML Models for Real-Time Inferencing

9. Chapter 7: Deploying ML Models for Batch Scoring

10. Chapter 8: Responsible AI

11. Chapter 9: Productionizing Your Workload with MLOps

12. Part 3: Productionizing Your Workload with MLOps

13. Chapter 10: Using Deep Learning in Azure Machine Learning

14. Chapter 11: Using Distributed Training in AMLS

15. Index

Why subscribe?

16. Other Books You May Enjoy

Summary

In this chapter, the focus was on deploying your model as a REST endpoint to support real-time inferencing use cases. We saw that we are able to leverage AMLS Studio for a low-code deployment experience. We also leveraged SDK v2 to deploy models to managed online endpoints. We continued by deploying models through CLI v2 to support model deployment for real-time inferencing. These sections demonstrated deploying real-time web services through low-code, code-first, and configuration-driven approaches. These capabilities empower you to deploy in a variety of ways.

In the next chapter, we will learn how to leverage batch-inferencing to support our use cases.

The rest of the chapter is locked

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Authors (5)

Sina Fakhraee Ph.D

Sina Fakhraee Ph.D

Sina Fakhraee, Ph.D., is currently working at Microsoft as an enterprise data scientist and senior cloud solution architect. He has helped customers to successfully migrate to Azure by providing best practices around data and AI architectural design and by helping them implement AI/ML solutions on Azure. Prior to working at Microsoft, Sina worked at Ford Motor Company as a product owner for Ford's AI/ML platform. Sina holds a Ph.D. degree in computer science and engineering from Wayne State University and prior to joining the industry, he taught various undergrad and grad computer science courses part time.

See other products by Sina Fakhraee Ph.D

Dennis Michael Sawyers

Dennis Michael Sawyers

Dennis Michael Sawyers is a senior cloud solutions architect (CSA) at Microsoft, specializing in data and AI. In his role as a CSA, he helps Fortune 500 companies leverage Microsoft Azure cloud technology to build top-class machine learning and AI solutions. Prior to his role at Microsoft, he was a data scientist at Ford Motor Company in Global Data Insight and Analytics (GDIA) and a researcher in anomaly detection at the highly regarded Carnegie Mellon Auton Lab. He received a master's degree in data analytics from Carnegie Mellon's Heinz College and a bachelor's degree from the University of Michigan. More than anything, Dennis is passionate about democratizing AI solutions through automated machine learning technology.

See other products by Dennis Michael Sawyers

Sina Fakhraee

Sina Fakhraee

Sina Fakhraee, Ph.D., is currently working at Microsoft as an enterprise data scientist and senior cloud solution architect. He has helped customers to successfully migrate to Azure by providing best practices around data and AI architectural design and by helping them implement AI/ML solutions on Azure. Prior to working at Microsoft, Sina worked at Ford Motor Company as a product owner for Ford's AI/ML platform. Sina holds a Ph.D. degree in computer science and engineering from Wayne State University and prior to joining the industry, he taught various undergrad and grad computer science courses part time.

See other products by Sina Fakhraee

Megan Masanz

Megan Masanz

Megan Masanz is a principal cloud solution architect at Microsoft focused on data, AI, and data science, passionately enabling organizations to address business challenges through the establishment of strategies and road maps for the planning, design, and deployment of Azure Cloud-based solutions. Megan is adept at paving the path to data science via computer science given her master's in computer science with a focus on data science.

See other products by Megan Masanz

Balamurugan Balakreshnan

Balamurugan Balakreshnan

Balamurugan Balakreshnan is a principal cloud solution architect at Microsoft Data/AI Architect and Data Science. He has provided leadership on digital transformations with AI and cloud-based digital solutions. He has also provided leadership in terms of ML, the IoT, big data, and advanced analytical solutions.

See other products by Balamurugan Balakreshnan

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