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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

Large-scale distributed model training

Think back to the discussion we had in the AI/ML and cloud computing section of Chapter 1, in which I described the process of scaling our models to larger sizes, starting with small models that we could train on our laptop, progressing to larger models trained on a powerful, high-end server, and eventually getting to the scale at which a single computer (even the most powerful server on the market) couldn’t handle either the size of the model or the dataset on which the model is trained. In this section, we’ll look at what it means to train such large-scale models in more detail.

We’ve covered the model training process in great detail throughout this book, but I’ll briefly summarize the process here as a knowledge refresher because these concepts are important when discussing large-scale distributed model training. For this discussion, I will focus on supervised training of neural networks.

The supervised training...

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