Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto
Authors: Eric Carter – Matthew Hurst
ISBN-10: 1484251067
ISBN-13: 9781484251065
Edition 版本: 1st ed.
Released: 2019-08-22
Print Length 页数: 268 pages
Book Description
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 You’ll 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
1.Early Delivery
2.Changing Requirements
3.Continuous Delivery
4.Aligning with the Business
5.Motivated Individuals
6.Effective Communication
7.Monitoring
8.Sustainable Development
9.Technical Excellence
10.Simplicity
11.Self-Organizing Teams
12.Tuning and Adjusting
13.Conclusion
