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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

Hyperparameter optimization

How do we know what kinds of hyperparameters to use and what their values should be? Hyperparameters can be chosen based on domain knowledge, experience, or trial and error, but to most efficiently choose the best hyperparameters, we can use a process called hyperparameter optimization, or hyperparameter tuning, which is a systematic process that can be implemented via different mechanisms that we will discuss next. Ultimately, the goal of hyperparameter optimization is to tune the hyperparameters of a model to achieve the best performance as measured by running it against a validation set, which is a subset of our source dataset.

Methods for optimizing hyperparameter values

In Chapter 2, we described hyperparameter tuning mechanisms such as grid search, random search, and Bayesian optimization, summarized here as a quick refresher:

  • Grid search: This is an exhaustive search of the entire hyperparameter space (i.e., it tries out every possible...
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