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Architecting Google Cloud Solutions

You're reading from   Architecting Google Cloud Solutions Learn to design robust and future-proof solutions with Google Cloud technologies

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563308
Length 472 pages
Edition 1st Edition
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Author (1):
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Victor Dantas Victor Dantas
Author Profile Icon Victor Dantas
Victor Dantas
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Introduction to Google Cloud
2. Chapter 1: An Introduction to Google Cloud for Architects FREE CHAPTER 3. Chapter 2: Mastering the Basics of Google Cloud 4. Section 2: Designing Great Solutions in Google Cloud
5. Chapter 3: Designing the Network 6. Chapter 4: Architecting Compute Infrastructure 7. Chapter 5: Architecting Storage and Data Infrastructure 8. Chapter 6: Configuring Services for Observability 9. Chapter 7: Designing for Security and Compliance 10. Section 3: Designing for the Modern Enterprise
11. Chapter 8: Approaching Big Data and Data Pipelines 12. Chapter 9: Jumping on the DevOps Bandwagon with Site Reliability Engineering (SRE) 13. Chapter 10: Re-Architecting with Microservices 14. Chapter 11: Applying Machine Learning and Artificial Intelligence 15. Chapter 12: Achieving Operational Excellence 16. Other Books You May Enjoy

Productionizing custom ML models with MLOps

ML systems have unique characteristics that differentiate them from traditional software. They require the testing and validation of both code and data, they have unique ways of measuring quality and evaluating performance, and deployed ML models typically degrade over time if they don't continuously evolve. Moreover, observability becomes difficult since systems can underperform without throwing errors or showing signs of it. Therefore, managing and operating ML models can be challenging.

In Chapter 9, Jumping on the DevOps Bandwagon with Site Reliability Engineering (SRE), we've discussed DevOps principles and how they can help improve the reliability of systems and shorten development cycles. As data science and ML became crucially important capabilities for modern enterprises, applying a similar set of principles to ML systems has become a priority for many. Hence, the Machine Learning Operations (MLOps) paradigm emerged...

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