Search icon CANCEL
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
AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

You're reading from   AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide The ultimate guide to passing the MLS-C01 exam on your first attempt

Arrow left icon
Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781835082201
Length 342 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Somanath Nanda Somanath Nanda
Author Profile Icon Somanath Nanda
Somanath Nanda
Weslley Moura Weslley Moura
Author Profile Icon Weslley Moura
Weslley Moura
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Machine Learning Fundamentals FREE CHAPTER 2. Chapter 2: AWS Services for Data Storage 3. Chapter 3: AWS Services for Data Migration and Processing 4. Chapter 4: Data Preparation and Transformation 5. Chapter 5: Data Understanding and Visualization 6. Chapter 6: Applying Machine Learning Algorithms 7. Chapter 7: Evaluating and Optimizing Models 8. Chapter 8: AWS Application Services for AI/ML 9. Chapter 9: Amazon SageMaker Modeling 10. Chapter 10: Model Deployment 11. Chapter 11: Accessing the Online Practice Resources 12. Other Books You May Enjoy

Amazon DynamoDB for NoSQL Database-as-a-Service

Amazon DynamoDB is a NoSQL database-as-a-service product within AWS. It’s a fully managed key/value and document database. Accessing DynamoDB is easy via its endpoint. The input and output throughputs can be managed or scaled manually or automatically. It also supports data backup, point-in-time recovery, and data encryption.

One example where Amazon DynamoDB can be used with Amazon SageMaker in a cost-efficient way is for real-time prediction applications. DynamoDB can serve as a storage backend for storing and retrieving input data for prediction models built using SageMaker. Instead of continuously running and scaling an inference endpoint, which can be costlier, you can leverage DynamoDB’s low-latency access and scalability to retrieve the required input data on demand.

In this setup, the input data for predictions can be stored in DynamoDB tables, where each item represents a unique data instance. When a prediction...

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