Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Azure Machine Learning Engineering

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

Arrow left icon
Product type Paperback
Published in Jan 2023
Publisher Packt
ISBN-13 9781803239309
Length 362 pages
Edition 1st Edition
Tools
Arrow right icon
Authors (4):
Arrow left icon
Balamurugan Balakreshnan Balamurugan Balakreshnan
Author Profile Icon Balamurugan Balakreshnan
Balamurugan Balakreshnan
Dennis Michael Sawyers Dennis Michael Sawyers
Author Profile Icon Dennis Michael Sawyers
Dennis Michael Sawyers
Sina Fakhraee Ph.D Sina Fakhraee Ph.D
Author Profile Icon Sina Fakhraee Ph.D
Sina Fakhraee Ph.D
Megan Masanz Megan Masanz
Author Profile Icon Megan Masanz
Megan Masanz
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

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 16. Other Books You May Enjoy

Summary

In this chapter, you have explored the options for creating an AMLS workspace, how to navigate in AMLS, how to create compute instances for developing code, and how to use your compute instance to develop code. You have dug into creating an AMLS workspace through the Azure portal, through the CLI, as well as through ARM templates. You have navigated through the AMLS workspace components, exploring the Author, Assets, and Manage sections of AMLS. You have explored a variety of IDEs, including Jupyter, AML notebooks, as well as VS Code, Microsoft’s premier IDE, with the compute instance that you created either using the portal, the CLI, or via an ARM template. This foundation gives you everything you need to begin scripting ML solutions.

In the next chapter, you will import data into AMLS and connect to a variety of data sources. You will also learn how to automatically track changes to your data and roll back to earlier versions if necessary.

You have been reading a chapter from
Azure Machine Learning Engineering
Published in: Jan 2023
Publisher: Packt
ISBN-13: 9781803239309
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime