Architecting Data and Machine Learning Platforms

Marco Tranquillin

ISBN 10: 1098151615 / ISBN 13: 9781098151614

Published by O'reilly Media Jan 2024, 2024

About this Item

Neuware -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.

Seller Inventory # 9781098151614

Bibliographic Details

• Title: Architecting Data and Machine Learning Platforms
• Publisher: O'reilly Media Jan 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 1098151615
• ISBN 13: 9781098151614
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 231W x 174H x 20D millimeters 231 millimeters width by 174 millimeters height by 20 millimeters depth
• Weight: 640 grams

About this title

Synopsis

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 to:

• Design a modern and secure cloud native or hybrid data analytics and machine learning platform
• Accelerate data-led innovation by consolidating enterprise data in a governed, scalable, and resilient data platform
• Democratize access to enterprise data and govern how business teams extract insights and build AI/ML capabilities
• Enable your business to make decisions in real time using streaming pipelines
• Build an MLOps platform to move to a predictive and prescriptive analytics approach

About the Authors

Marco is a seasoned digital consultant with over 10 years of experience in helping organizations around the world implement digital transformation strategies using cloud computing. In his last role at Google Cloud, he led a team of Principal Architects and Customer Engineers who helped Italian financial and insurance firms adopt and use cloud data technologies to solve business problems. He also previously led the European Data Analytics practice at Google Cloud and held consulting roles at other major firms such as Microsoft and Accenture.

Lak works with management and data teams across a range of industries to help them employ data and AI-driven innovation to grow their businesses and increase value. Prior to this, Lak was the Director for Data Analytics and AI Solutions on Google Cloud and a Research Scientist at NOAA. He is a co-author of Data Science on the Google Cloud Platform, BigQuery: The Definitive Guide, and Machine Learning Design Patterns, all published by O’Reilly.

Firat is an adjunct professor at the University of Manchester and a Senior Product Manager in Google Cloud. Firat has over 20 years of experience in designing and delivering bespoke information systems for some of the world's largest research, education, telecommunications, finance and retail organizations. Following roles within National Supercomputing Services and National Centre for Text Mining, he has over 30 publications in the areas of Parallel Computing, Big Data, Artificial Intelligence and Computer Communications.