Description
Book SynopsisGain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as 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 and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data pro
Table of ContentsPart I. Getting Started1. The Problem with Data Science2. Data Strategy
Part II. Toward DataOps3. Lean Thinking4. Agile Collaboration5. Build Feedback and Measurement
Part III. Further Steps6. Building Trust7. DevOps for DataOps8. Organizing for DataOps
Part IV. The Self-Service Organization9. DataOps Technology10. The DataOps Factory
