Google Machine Learning and Generative AI for Solutions Architects: ​Build efficient and scalable AI/ML solutions on Google Cloud

English | 2024 | ISBN: 978-1803245270 | 552 Pages | EPUB | 14 MB

Architect and run real-world AI/ML solutions at scale on Google Cloud, and discover best practices to address common industry challenges effectively

Key Features:

• Understand key concepts, from fundamentals through to complex topics, via a methodical approach
• Build real-world end-to-end MLOps solutions and generative AI applications on Google Cloud
• Get your hands on a code repository with over 20 hands-on projects for all stages of the ML model development lifecycle

Nearly all companies nowadays either already use or are trying to incorporate AI/ML into their businesses. While AI/ML research is undoubtedly complex, the building and running of apps that utilize AI/ML effectively is tougher. This book shows you exactly how to design and run AI/ML workloads successfully using years of experience some of the world’s leading tech companies have to offer.

You’ll begin by gaining a clear understanding of essential fundamental AI/ML concepts, before moving on to grasp complex topics with the help of examples and hands-on activities. This will help you eventually explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. As you advance, you’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these challenges. The chapters also teach you about the vast AI/ML landscape on Google Cloud and how to implement all the steps needed in a typical AI/ML project. You’ll use services such as BigQuery to prepare data; Vertex AI to train, deploy, monitor, and scale models in production; as well as MLOps to automate the entire process.

By the end of this book, you will be able to unlock the full potential of Google Cloud’s AI/ML offerings.

What You Will Learn:

• Build solutions with open-source offerings on Google Cloud, such as TensorFlow, PyTorch, and Spark
• Source, understand, and prepare data for ML workloads
• Build, train, and deploy ML models on Google Cloud
• Create an effective MLOps strategy and implement MLOps workloads on Google Cloud
• Discover common challenges in typical AI/ML projects and get solutions from experts
• Explore vector databases and their importance in Generative AI applications
• Uncover new Gen AI patterns such as Retrieval Augmented Generation (RAG), agents, and agentic workflows