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
The Machine Learning Solutions Architect Handbook

You're reading from   The Machine Learning Solutions Architect Handbook Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI

Arrow left icon
Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Length 602 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
David Ping David Ping
Author Profile Icon David Ping
David Ping
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Preface 1. Navigating the ML Lifecycle with ML Solutions Architecture FREE CHAPTER 2. Exploring ML Business Use Cases 3. Exploring ML Algorithms 4. Data Management for ML 5. Exploring Open-Source ML Libraries 6. Kubernetes Container Orchestration Infrastructure Management 7. Open-Source ML Platforms 8. Building a Data Science Environment Using AWS ML Services 9. Designing an Enterprise ML Architecture with AWS ML Services 10. Advanced ML Engineering 11. Building ML Solutions with AWS AI Services 12. AI Risk Management 13. Bias, Explainability, Privacy, and Adversarial Attacks 14. Charting the Course of Your ML Journey 15. Navigating the Generative AI Project Lifecycle 16. Designing Generative AI Platforms and Solutions 17. Other Books You May Enjoy
18. Index

Building ML Solutions with AWS AI Services

Up to this point, we have mainly focused on the skills and technologies required to build and deploy ML models using open-source technologies and managed ML platforms. To solve business problems with ML, however, you don’t always have to build, train, and deploy your ML models from scratch. An alternative option is to use fully managed AI services. AI services are fully managed APIs or applications with pre-trained models that perform specific ML tasks, such as object detection or sentiment analysis. Some AI services also allow you to train custom models with your data for a defined ML task, such as document classification. AI services promise to enable organizations to build ML-enabled solutions without requiring strong ML competencies.

In this chapter, we are going to switch gears and talk about several AWS AI services and where they can be used in business applications. Please note that the focus of this chapter will not be...

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