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

Training on a compute instance

You can train a model on a compute instance or on a compute cluster. In this section, we will use our existing compute instance before continuing with training on a compute cluster.

To begin working with a compute instance, we will need to turn on the compute instance that was created in Chapter 1, Introducing the Azure Machine Learning Service.

Follow these steps to train a model on a compute instance within AMLS:

  1. Go to https://ml.azure.com.
  2. Select your workspace name.
  3. On the left side of the workspace, click Compute:

Figure 3.20 – Compute instance icon

  1. On the Compute screen, select your compute instance and select Start:

Figure 3.21 – Start compute

Your compute instance will change from Stopped to Starting status. Once the compute instance moves from Starting to Running status, it is ready for use.

  1. Click on the Terminal blue hyperlink under applications...
lock icon The rest of the chapter is locked
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