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
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Automated Machine Learning

You're reading from   Automated Machine Learning Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Arrow left icon
Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800567689
Length 312 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Adnan Masood Adnan Masood
Author Profile Icon Adnan Masood
Adnan Masood
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Automated Machine Learning
2. Chapter 1: A Lap around Automated Machine Learning FREE CHAPTER 3. Chapter 2: Automated Machine Learning, Algorithms, and Techniques 4. Chapter 3: Automated Machine Learning with Open Source Tools and Libraries 5. Section 2: AutoML with Cloud Platforms
6. Chapter 4: Getting Started with Azure Machine Learning 7. Chapter 5: Automated Machine Learning with Microsoft Azure 8. Chapter 6: Machine Learning with AWS 9. Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot 10. Chapter 8: Machine Learning with Google Cloud Platform 11. Chapter 9: Automated Machine Learning with GCP 12. Section 3: Applied Automated Machine Learning
13. Chapter 10: AutoML in the Enterprise 14. Other Books You May Enjoy

Introducing Microsoft NNI

Microsoft Neural Network Intelligence (NNI) is an open source platform that addresses the three key areas of any automated ML life cycle – automated feature engineering, architectural search (also referred to as neural architectural search or NAS), and hyperparameter tuning (HPI). The toolkit also offers model compression features and operationalization. NNI comes with many hyperparameter tuning algorithms already built in.

A high-level architecture diagram of NNI is as follows:

Figure 3.26 – Microsoft NNI high-level architecture

NNI has several state-of-the-art hyperparameter optimization algorithms built in, and they are called tuners. The list includes TPE, Random Search, Anneal, Naive Evolution, SMAC, Metis Tuner, Batch Tuner, Grid Search, GP Tuner, Network Morphism, Hyperband, BOHB, PPO Tuner, and PBT Tuner.

The toolkit is available on GitHub to be downloaded: https://github.com/microsoft/nni. More information...

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
Banner background image