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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

Summary

In this chapter, we explored the concepts of bias, explainability, fairness, and lineage. We started off by examining some of the common types of bias that can occur at various steps in the ML model development life cycle. This included sources of bias such as pre-existing bias, algorithmic bias, and collection or measurement bias, which further included sub-categories such as sampling bias, response bias, and observer bias. We talked about how to inspect for bias, using techniques such as data exploration and DIA.

Next, we dived into the use of explainability techniques to understand how our models make their decisions at inference time and to assess their fairness, particularly with regard to understanding how the input features in our dataset could influence our models’ predictions. We used tools such as PDPs and SHAP for these purposes. We then looked at how to use Vertex AI to get explanations from models that were hosted on Vertex AI endpoints. Going beyond...

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Google Machine Learning and Generative AI for Solutions Architects
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