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Google Machine Learning and Generative AI for Solutions Architects

Google Machine Learning and Generative AI for Solutions Architects

By : Kieran Kavanagh
4.9 (7)
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Google Machine Learning and Generative AI for Solutions Architects

Google Machine Learning and Generative AI for Solutions Architects

4.9 (7)
By: Kieran Kavanagh

Overview of this book

Most companies today are incorporating AI/ML into their businesses. Building and running apps utilizing AI/ML effectively is tough. This book, authored by a principal architect with about two decades of industry experience, who has led cross-functional teams to design, plan, implement, and govern enterprise cloud strategies, shows you exactly how to design and run AI/ML workloads successfully using years of experience from some of the world’s leading tech companies. You’ll get a clear understanding of essential fundamental AI/ML concepts, before moving on to complex topics with the help of examples and hands-on activities. This will help you explore advanced, cutting-edge AI/ML applications that address real-world use cases in today’s market. You’ll recognize the common challenges that companies face when implementing AI/ML workloads, and discover industry-proven best practices to overcome these. 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.
Table of Contents (24 chapters)
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1
Part 1:The Basics
5
Part 2:Diving in and building AI/ML solutions
17
Part 3:Generative AI

AI/ML Concepts, Real-World Applications, and Challenges

This chapter will introduce basic concepts that will be explored in more detail throughout the rest of the book. We understand that readers of this book may be starting from different stages in their artificial intelligence/machine learning (AI/ML) journey, whereby some readers may already be advanced practitioners who are familiar with running AI/ML workloads while others may be newer to AI/ML in general. For this reason, we will briefly describe important fundamental concepts as required throughout the book to ensure that all readers have a common baseline upon which to build their understanding of the topics we discuss. Readers who are newer to AI/ML will benefit from learning the important underlying concepts rather than diving straight into the deep end of each topic without a baseline context, and advanced practitioners should find them to be useful knowledge refreshers.

In this chapter, we’re going to cover the...

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Google Machine Learning and Generative AI for Solutions Architects
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