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tranquillin m architecting data machine learning platforms 2023 final

Type:: E-books
Files:: 1
Size:: 7.9 MB
Uploaded On:: Oct. 21, 2023, 12:55 p.m.
Added By:: andryold1
Seeders:: 6
Leechers:: 2
Info Hash:: C3CACD7BE777D0059E15E2C6CA9C8F5410F4A8FD

Textbook in PDF format What is a data platform? Why do you need it? What does building a data and Machine Learning (ML) platform involve? Why should you build your data platform on the cloud? This book starts by answering these common questions that arise when dealing with data and ML projects. We then lay out the strategic journey that we recommend you take to build data and ML capabilities in your business, show you how to execute on each step of that strategy, and wrap up all the concepts in a model data modernization case. 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

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Tranquillin M. Architecting Data and Machine Learning Platforms...2023.pdf 7.9 MB

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tranquillin m architecting data machine learning platforms 2023 final

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