Architecting Data and Machine Learning Platforms

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 faster 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 cloud-native/hybrid 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.