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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 took many of the ML concepts from Chapters 1 and 2 and put them into practice. We used clustering to find patterns in our data in an unsupervised manner, and you specifically learned a lot more about how K-means is used for clustering and how it works.

We then dived into SL, and you explored the linear regression class within scikit-learn and learned how to use metrics to measure the performance of a regression model.

Next, you learned how to use XGBoost to build a classification model and classify items in the iris dataset based on their features.

Not only did you put all of those important concepts into practice, but you also learned how to create and use Vertex AI Workbench-managed notebooks.

Additionally, you learned other important concepts in the ML industry, such as how decision trees work, how Gradient Boosting works, and how XGBoost enhances that functionality to implement one of the most effective ML algorithms in the industry.

That...

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Google Machine Learning and Generative AI for Solutions Architects
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