Presents a practical introduction to DataOps, a new discipline for delivering data science at scale used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action or useful data products. This book provides the approach and methods to ensure continuous rapid use of data to create analytical applications, steer decision making, and derive learning in order to satisfy the customer. shows how optimize the data supply chain from raw data to final analytical application, whether the goal is a machine learning model or other data-orientated output. Adapting and borrowing principles from Agile and DevOps methods, the book provides an approach to align data science with the rest of the organization and rapidly increase the number of reproducible analytics projects through automation and integration. helps you to improve the speed and accuracy at which analytical applications can be developed through data management practices that securely expand data access, and through lean thinking that improves quality via rigorous focus on testing, measurement, and eliminating wasted effort. The book shows how to collect feedback and monitor performance to manage and continuously improve your data products based on how they are used.  is for data science and advanced analytics experts, CIOs, CDOs (Chief Data Officers), Chief Analytics Officers, business analysts, business team leaders, and IT professionals (Data Engineers, Developers, Architects and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals facing barriers in getting data science and machine learning into customer-facing production, who are looking for an approach to rapidly deliver and scale deployments, and learn from outcomes. weiterlesen