The Definitive Guide to Google Vertex AI: Implement Machine Learning pipelines with Google Cloud Vertex AI

ISBN-13: 9781801815260 / Angielski / Miękka / 2023 / 469 str.

Jasmeet Bhatia;Kartik Chaudhary

Opis

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.