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The Definitive Guide to Google Vertex AI

You're reading from   The Definitive Guide to Google Vertex AI Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781801815260
Length 422 pages
Edition 1st Edition
Tools
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Authors (2):
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Kartik Chaudhary Kartik Chaudhary
Author Profile Icon Kartik Chaudhary
Kartik Chaudhary
Jasmeet Bhatia Jasmeet Bhatia
Author Profile Icon Jasmeet Bhatia
Jasmeet Bhatia
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Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1:The Importance of MLOps in a Real-World ML Deployment
2. Chapter 1: Machine Learning Project Life Cycle and Challenges FREE CHAPTER 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

Orchestrating ML workflows using Cloud Composer (managed Airflow)

Cloud Composer is a workflow orchestration service on Google Cloud that is built upon the open source project of Apache Airflow. The key difference is that Composer is fully managed and also integrates with other GCP tooling very easily. With Cloud Composer, we can write, execute, schedule, or monitor our workflows that are also supported across multi-cloud and hybrid environments. Composer pipelines are DAGs that can be easily defined and configured using Python. It comes with a rich library of connectors that let us deploy our workflows instantly with one click. Graphical representations of workflows on the Google Cloud console make monitoring and troubleshooting quite convenient. Automatic synchronization of our DAGs ensures that our jobs always stay on schedule.

Cloud Composer is commonly used by data scientists and data engineers to build complex data pipelines (ETL or ELT pipelines). It can also be used as an...

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