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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

Feature engineering

Feature engineering can constitute a large portion of a data scientist’s activities, and it can be just as important to their success, or sometimes even more important, than choosing the right machine learning algorithm. In this section, we will dive deeper into feature engineering, which can be considered both an art and a science.

We will use the Titanic dataset available on OpenML (https://www.openml.org/search?type=data&sort=runs&id=40945) for our examples in this section. This dataset contains information about passengers aboard the Titanic, including demographic data, ticket class, fare, and whether they survived the sinking of the ship.

In the Chapter-07 directory in JupyterLab on your Vertex AI Workbench Notebook Instance, open the feature-eng-titanic.ipynb notebook and choose Python (Local) as the kernel. Again, run each cell in the notebook by selecting the cell and pressing Shift + Enter on your keyboard.

In this notebook, the...

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