| Summary: | Nearly 7,000 new mobile applications appear every day, and a constant stream of data gives them life. Many organizations rely on a predictive analytics model to turn data into useful business information and ensure the predictions remain accurate as data changes. It can be a complex, time-consuming process. This book shows how to automate and accelerate that process using machine learning (ML) on a modern data warehouse that runs on any cloud. Product specialists from MemSQL explain how today's modern data warehouses provide the foundations to implement ML algorithms that run efficiently. Through several real-time use cases, you'll learn how to quickly identify the right metrics to make actionable business decisions. This book explores foundational ML and artificial intelligence concepts to help you understand: How data warehouses accelerate deployment and simplify manageability How companies make a choice between cloud and on-premises deployments for building data processing applications Ways to build analytics and visualizations for business intelligence on historical data The technologies and architecture for building and deploying real-time data pipelines This book demonstrates specific models and examples for building supervised and unsupervised real-time ML applications, and gives practical advice on how to make the choice between building an ML pipeline or buying an existing solution. If you need to use data accurately and efficiently, a real-time data warehouse is a critical business tool
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