Agile Machine Learning : effective machine learning inspired by the Agile Manifesto Chapter 1: Early Delivery Chapter 2: Changing Requirements Chapter 3: Continuous Delivery Chapter 4: Aligning with the Business Chapter 5: Motivated Individuals Chapter 6: Effective Communication Chapter 7: Monitoring Chapter 8: Sustainable Development Chapter 9: Technical Excellence Chapter 10 Simplicity Chapter 11: Self organizing Teams Chapter 12: Tuning and Adjusting Chapter 13: Conclusion Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn by example how to organize and manage a fast paced team challenged with solving novel data problems at scale in a production environment. The authors’ approach models the ground breaking engineering principles described in the Agile Manifesto. The book provides further context and contrasts the original principles with the requirements of systems that deliver a data product. What Youll Learn: Effectively run a data engineering team that is metrics focused experiment focused and data focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy a key attribute of a reliable data engineer from definitions to expectations This book is for anyone who manages a machine learning team or is responsible for creating production ready inference components. Anyone responsible for data project workflow of sampling data; labeling training testing improving and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data. Eric Carter is Partner Group Engineering Manager on the Cortana Engineering team at Microsoft. In this role he works on search features around email and calendar and implements new compliant versions of Cortana hosted in Azure and Microsofts Office 365 substrate environments. He is also responsible for delivering frameworks patterns and practices for services development to other teams. Matthew Hurst is Principal Architect on the Bing Local Search team at Microsoft. In this role he leads a team mining the web for knowledge about local businesses and attractions serving local queries in multiple markets. He has both managed the team and acted as technical lead and is involved in collaborating with multiple teams including Microsoft Research