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The Definitive Guide to Google Vertex AI

You're reading from  The Definitive Guide to Google Vertex AI

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781801815260
Pages 422 pages
Edition 1st Edition
Languages
Authors (2):
Jasmeet Bhatia Jasmeet Bhatia
Profile icon Jasmeet Bhatia
Kartik Chaudhary Kartik Chaudhary
Profile icon Kartik Chaudhary
View More author details

Table of Contents (24) Chapters

Preface 1. Part 1:The Importance of MLOps in a Real-World ML Deployment
2. Chapter 1: Machine Learning Project Life Cycle and Challenges 3. Chapter 2: What Is MLOps, and Why Is It So Important for Every ML Team? 4. Part 2: Machine Learning Tools for Custom Models on Google Cloud
5. Chapter 3: It’s All About Data – Options to Store and Transform ML Datasets 6. Chapter 4: Vertex AI Workbench – a One-Stop Tool for AI/ML Development Needs 7. Chapter 5: No-Code Options for Building ML Models 8. Chapter 6: Low-Code Options for Building ML Models 9. Chapter 7: Training Fully Custom ML Models with Vertex AI 10. Chapter 8: ML Model Explainability 11. Chapter 9: Model Optimizations – Hyperparameter Tuning and NAS 12. Chapter 10: Vertex AI Deployment and Automation Tools – Orchestration through Managed Kubeflow Pipelines 13. Chapter 11: MLOps Governance with Vertex AI 14. Part 3: Prebuilt/Turnkey ML Solutions Available in GCP
15. Chapter 12: Vertex AI – Generative AI Tools 16. Chapter 13: Document AI – An End-to-End Solution for Processing Documents 17. Chapter 14: ML APIs for Vision, NLP, and Speech 18. Part 4: Building Real-World ML Solutions with Google Cloud
19. Chapter 15: Recommender Systems – Predict What Movies a User Would Like to Watch 20. Chapter 16: Vision-Based Defect Detection System – Machines Can See Now! 21. Chapter 17: Natural Language Models – Detecting Fake News Articles! 22. Index 23. Other Books You May Enjoy

What Is MLOps, and Why Is It So Important for Every ML Team?

Machine learning operations (MLOps) is a pivotal practice for modern ML teams, encompassing the blend of technological and operational best practices. At its heart, MLOps seeks to address the challenges of productionizing ML models and fostering better collaboration between data scientists and IT teams. With the rapid advancements in technology and increasing reliance on ML solutions, MLOps is becoming the backbone of a sustainable and scalable ML strategy. This chapter will delve deep into the essence of MLOps, detailing its significance, its various maturity levels, and the role of Google’s Vertex AI in facilitating MLOps. By the end of this chapter, you will be equipped with a robust understanding of MLOps principles and what tools in Vertex AI can be used to implement those principles.

In this chapter, we will cover the following topics:

  • Why is MLOps important?
  • MLOps maturity levels
  • How can Vertex...
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