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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, you learned how to ingest data into Google Cloud from various sources, and you discovered important concepts on how to process data in Google Cloud.

You then learned about exploring and visualizing data using Vertex AI and BigQuery. Next, you learned how to clean and prepare data for ML workloads using Jupyter notebooks, and then how to create an automated data pipeline to perform the same transformations at a production scale in a batch method using Apache Spark on Google Cloud Dataproc, as well as how to automatically orchestrate that entire process using Apache Airflow in GCC.

We then covered important concepts and tools related to processing streaming data, and you finally built your own streaming data processing pipelines using Apache Beam on Google Cloud Dataflow.

In the next chapter, we will spend additional time on data processing and preparation, with a specific focus on the concept of feature engineering.

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Google Machine Learning and Generative AI for Solutions Architects
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