The Definitive Guide to Google Vertex AI: Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices - Softcover

Inhaltsangabe

Implement Machine Learning pipelines with Google Cloud Vertex AI

Key Features

• Understand the importance of an AI platform and MLOPs practices for Machine Learning projects
• Learn Google Vertex AI tools and offerings for accelerating your end-to-end ML solution journey
• Implement Vision, NLP and Recommendation based real-world ML models on Google Cloud Platform

Book Description

Google Cloud vertex AI is still relatively new product and has rapidly launched number of new features in last 6-12 months. There are no comprehensive books out there in the market right now which cover the newer features like Feature store, Metadata Store, Model registry, Managed Notebooks, Matching Engine etc.

Since Google has now combined almost all different custom ML products under Vertex AI umbrella, there is a big push by Google to encourage more people to get certified on Vertex AI, thereby creating a huge demand for such books.

What you will learn

• Get quickly started on ML model development using Google Vertex AI
• Learn how to manage datasets, artifacts and experiments
• Develop no-code, low-code to Custom AI solution on Google Cloud
• Learn to implement advanced model optimization techniques and tooling
• Understand pre-build/turnkey AI solution offerings from Google
• Learn to build and deploy custom ML models for real-world applications

Who This Book Is For

If you are a Machine Learning practitioner who wants to learn end-to-end ML solution development journey on Google Cloud Platform using MLOPs best practices and tools offered by Google Vertex AI, this book is for you. Starting from data storage, data management, this book takes you through Vertex AI offerings for building, experimenting, optimizing and deploying ML solutions in a fast and scalable way using Google best practices. It also covers topics related to scaling, monitoring and governing your ML Workloads with the help of MLOPs tooling on Google Cloud.

Table of Contents

1. Machine Learning project lifecycle and challenges
2. What is ML Ops and why is it important for every organization using ML?
3. It’s all about Data - Options to store and transform ML datasets
4. Vertex AI Workbench - One stop tool for all your ML Development needs
5. Training ML Models - No code option - AutoML
6. Training ML Models -Low code option - BQML
7. Training fully custom ML models with Vertex AI
8. Model Explainability through Vertex AI
9. Model optimizations – Hyper parameter tuning and NAS (TBD)
10. Vertex AI Deployment and Automation tools – Orchestration through managed Kubeflow pipelines
11. Vertex AI Governance Tools
12. Retail AI – Product discovery and recommendation system
13. Document AI – Customizable document processing solution
14. ML APIs – Vision API, NLP API etc.
15. Recommender Systems - Predict what movie would you like to watch?
16. Vision based defect detection system - Machines can see now.
17. Natural Language Models – Detecting fake news articles!
18. Using GANs to generate images – Don’t believe everything you see! (TBD)

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