Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud

by Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner

All cloud architects need to know how to build data platforms that enable businesses to make data-driven decisions and deliver enterprise-wide intelligence in a fast and efficient way. This handbook shows you how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, and multicloud tools like Snowflake and Databricks. Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle from ingestion to activation in a cloud environment using real-world enterprise architectures. You'll learn how to transform, secure, and modernize familiar solutions like data warehouses and data lakes, and you'll be able to leverage recent AI/ML patterns to get accurate and quicker insights to drive competitive advantage. You'll learn how

Genres: Artificial Intelligence

359 pages, Paperback

Published November 21, 2023

Reviews

[Mohammad Forouhesh · Rating 4 out of 5]

Not at all for professionals in the field, but good content to begin your journey into becoming a well-versed ML engineer. I am planning to use parts of it as supplementary material in our onboarding process for my team, specifically chapter 11, Architecting an ML Platform, which touches on many important concepts in any modern AI-enabled platform. It will give you a good understanding of the overall terminologies and designs to productionize/monetize machine learning models in the real world.

All in all, I was disappointed, partly because the title hints at advanced content. So I give it a 3.75 score.

[Ferhat Culfaz · Rating 3 out of 5]

Covers many areas of interest for solution architects. However I found James Serra Deciphering Data Architectures more accessible for my needs as a Data Scientist. I’m sure other people will find this book by Tranquillin et al.