Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Mastering Azure Machine Learning

You're reading from   Mastering Azure Machine Learning Execute large-scale end-to-end machine learning with Azure

Arrow left icon
Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781803232416
Length 624 pages
Edition 2nd Edition
Tools
Arrow right icon
Authors (2):
Arrow left icon
Marcel Alsdorf Marcel Alsdorf
Author Profile Icon Marcel Alsdorf
Marcel Alsdorf
Christoph Körner Christoph Körner
Author Profile Icon Christoph Körner
Christoph Körner
Arrow right icon
View More author details
Toc

Table of Contents (23) Chapters Close

Preface 1. Section 1: Introduction to Azure Machine Learning
2. Chapter 1: Understanding the End-to-End Machine Learning Process FREE CHAPTER 3. Chapter 2: Choosing the Right Machine Learning Service in Azure 4. Chapter 3: Preparing the Azure Machine Learning Workspace 5. Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
6. Chapter 4: Ingesting Data and Managing Datasets 7. Chapter 5: Performing Data Analysis and Visualization 8. Chapter 6: Feature Engineering and Labeling 9. Chapter 7: Advanced Feature Extraction with NLP 10. Chapter 8: Azure Machine Learning Pipelines 11. Section 3: The Training and Optimization of Machine Learning Models
12. Chapter 9: Building ML Models Using Azure Machine Learning 13. Chapter 10: Training Deep Neural Networks on Azure 14. Chapter 11: Hyperparameter Tuning and Automated Machine Learning 15. Chapter 12: Distributed Machine Learning on Azure 16. Chapter 13: Building a Recommendation Engine in Azure 17. Section 4: Machine Learning Model Deployment and Operations
18. Chapter 14: Model Deployment, Endpoints, and Operations 19. Chapter 15: Model Interoperability, Hardware Optimization, and Integrations 20. Chapter 16: Bringing Models into Production with MLOps 21. Chapter 17: Preparing for a Successful ML Journey 22. Other Books You May Enjoy

ML operations in Azure

You successfully registered a trained model, an environment, a scoring file, and an inference configuration in the previous section. You optimized your model for scoring and deployed it to a managed Kubernetes cluster. You autogenerated client SDKs for your ML services. So, can you finally lean back and enjoy the success of your hard work? Well, not yet! First, we need to make sure that we have all our monitoring in place so that we can observe and react to anything happening to our deployment.

First, the good points: with Azure Machine Learning deployments and managed compute targets, you will get many things included out of the box with either Azure, Azure Machine Learning, or your service used as a compute target. Tools such as the Azure Dashboard on the Azure Portal, Azure Monitor, and Azure Log Analytics make it easy to centralize log and debug information. Once your data is available through Log Analytics, it can be queried, analyzed, visualized, alerted...

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