Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud (Final)КНИГИ » ОС И БД
Название: Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud (Final) Автор: Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner Издательство: O’Reilly Media, Inc. Год: 2024 Страниц: 362 Язык: английский Формат: pdf (true), epub (true) Размер: 16.4 MB
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
Who Is This Book For? This book is for architects who wish to support data-driven decision making in their business by creating a data and ML platform using public cloud technologies. Data engineers, data analysts, data scientists, and ML engineers will find the book useful to gain a conceptual design view of the systems that they might be implementing on top of.
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Data Engineering on Azure Название: Data Engineering on Azure Автор: Vlad Riscutia Издательство: Manning Publications Год: 2021 Страниц: 335 Язык: английский Формат: epub...
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Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud (Final) 
