
Architecting Data and Machine Learning Platforms
Enable Analytics and Ai-Driven Innovation in the Cloud
O'Reilly (Publisher)
Published on 2. January 2024
Book
Paperback/Softback
300 pages
978-1-0981-5161-4 (ISBN)
Description
All cloud architects need to know how to build data platforms-the key to enabling businesses with data and delivering enterprise-wide intelligence in a fast and efficient way. This handbook is ideal for learning how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, or multicloud tools like Fivetran, dbt, Snowflake, and Databricks.
Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle in a cloud environment, from ingestion to activation, using real-world enterprise architectures. You'll learn how to transform 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.
This book shows you how to:
Design a modern cloud native or hybrid data analytics and machine learning platform
Accelerate data-led innovation by consolidating enterprise data in a data platform
Democratize access to enterprise data and allow business teams to extract insights and build AI/ML capabilities
Enable your business to make decisions in real time using streaming pipelines
Move from a descriptive analytics approach to a more predictive and prescriptive one by building an MLOps platform
Make your organization more effective in working with data analytics and machine learning in a cloud environment
Authors Marco Tranquillin, Valliappa Lakshmanan, and Firat Tekiner cover the entire data lifecycle in a cloud environment, from ingestion to activation, using real-world enterprise architectures. You'll learn how to transform 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.
This book shows you how to:
Design a modern cloud native or hybrid data analytics and machine learning platform
Accelerate data-led innovation by consolidating enterprise data in a data platform
Democratize access to enterprise data and allow business teams to extract insights and build AI/ML capabilities
Enable your business to make decisions in real time using streaming pipelines
Move from a descriptive analytics approach to a more predictive and prescriptive one by building an MLOps platform
Make your organization more effective in working with data analytics and machine learning in a cloud environment
More details
Language
English
Place of publication
Sebastopol
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 231 mm
Width: 174 mm
Thickness: 20 mm
Weight
640 gr
ISBN-13
978-1-0981-5161-4 (9781098151614)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Marco Tranquillin | Valliappa Lakshmanan | Firat Tekiner
Architecting Data and Machine Learning Platforms
E-Book
10/2023
O'Reilly
€55.99
Available for download

Marco Tranquillin | Valliappa Lakshmanan | Firat Tekiner
Architecting Data and Machine Learning Platforms
E-Book
10/2023
O'Reilly
€55.99
Available for download
Persons
Marco is leading a Principal Architect and Customer Engineering team at Google Cloud who helps Italian financial and insurance firms to adopt and leverage cloud data technologies to solve business problems. In the past he led the European Data Analytics practice within Google Cloud and has more than 10 years of experience working in complex IT cloud projects for many global firms. 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.