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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 discussed various Google Cloud services for hosting ML models, such as Vertex AI, Cloud Functions, GKE, and Cloud Run. We differentiated between online and offline model serving, whereby online serving is used for real-time predictions, and offline serving is used for batch predictions. Then, we explored common challenges in deploying ML models, such as data/model drift, scaling, monitoring, performance, and keeping our models up to date. We also introduced specific components of Vertex AI that make it easier for us to deploy and manage models, such as the Vertex AI Model Registry, the Vertex AI prediction service, and Vertex AI Model Monitoring.

Specifically, we dived quite deep into monitoring models in production, focusing on data drift and model drift. We discussed mechanisms to combat these drifts, such as automated continuous training.

Next, we explained A/B testing for comparing two versions of a model, and we discussed optimizing ML models...

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Google Machine Learning and Generative AI for Solutions Architects
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