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
DevOps for Databases

You're reading from   DevOps for Databases A practical guide to applying DevOps best practices to data-persistent technologies

Arrow left icon
Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781837637300
Length 446 pages
Edition 1st Edition
Concepts
Arrow right icon
Author (1):
Arrow left icon
David Jambor David Jambor
Author Profile Icon David Jambor
David Jambor
Arrow right icon
View More author details
Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1: Database DevOps
2. Chapter 1: Data at Scale with DevOps FREE CHAPTER 3. Chapter 2: Large-Scale Data-Persistent Systems 4. Chapter 3: DBAs in the World of DevOps 5. Part 2: Persisting Data in the Cloud
6. Chapter 4: Cloud Migration and Modern Data(base) Evolution 7. Chapter 5: RDBMS with DevOps 8. Chapter 6: Non-Relational DMSs with DevOps 9. Chapter 7: AI, ML, and Big Data 10. Part 3: The Right Tool for the Job
11. Chapter 8: Zero-Touch Operations 12. Chapter 9: Design and Implementation 13. Chapter 10: Database Automation 14. Part 4: Build and Operate
15. Chapter 11: End-to-End Ownership Model – a Theoretical Case Study 16. Chapter 12: Immutable and Idempotent Logic – A Theoretical Case Study 17. Chapter 13: Operators and Self-Healing Data Persistent Systems 18. Chapter 14: Bringing Them Together 19. Part 5: The Future of Data
20. Chapter 15: Specializing in Data 21. Chapter 16: The Exciting New World of Data 22. Index 23. Other Books You May Enjoy

Definitions and applications of AI, ML, and big data

AI is a branch of computer science that focuses on creating intelligent machines that can perform tasks that would typically require human intelligence. AI systems can analyze data, recognize patterns, and make decisions based on that analysis. Some examples of AI applications include speech recognition, computer vision, natural language processing, robotics, and expert systems.

ML is a branch of AI that concentrates on creating algorithms that can learn from given data and enhance their efficiency as time progresses. ML algorithms can automatically identify patterns in data and use them to make predictions or decisions. Some examples of ML applications include predictive analytics, fraud detection, recommender systems, image recognition, and autonomous vehicles.

Big data refers to the large and complex sets of data that are generated by modern technology. This data is often unstructured, diverse, and difficult to process using...

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