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"Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can't scale data science teams fast enough to keep up with the growing amounts of data to transform. What's the answer? Self-service data. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work."--Publisher's description
About the author
Dr. Sandeep Uttamchandani is the Chief Data Officer and VP of Product Engineering at Unravel Data Systems. He brings nearly two decades of experience building enterprise data products as well as running petabyte-scale data platforms for business-critical analytics and ML applications. Most recently he was at Intuit, where he ran the data platform team powering analytics and ML for Intuit's financial accounting, payroll, and payments products. Previously in his career, Sandeep was co-founder and CEO of a startup using ML for managing security vulnerabilities of open-source products. He has played engineering leadership roles at VMware and IBM for 15+ years.
Sandeep holds more than 40 issued patents, has 25+ publications in key technical conferences, and has received several product innovation and management excellence awards. He is a regular speaker in data conferences and a guest lecturer at universities. He advises startups and has served as a program/steering committee member for several conferences, including serving as Co-chair of Gartner's SF CDO Executive Summit, and Usenix Operational ML (OpML) conference. Sandeep holds a Ph.D and a Master's in Computer Science from the University of Illinois at Urbana-Champaign.
Summary
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data.
