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Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud - Softcover
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
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
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Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud
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Paperback. Condition: new. Paperback. 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 About the AuthorsMarco 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781098151614
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