Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto

© Eric Carter, Matthew Hurst 2019

E. Carter, M. HurstAgile Machine Learninghttps://doi.org/10.1007/978-1-4842-5107-2_12

12. Tuning and Adjusting

Eric Carter¹  and Matthew Hurst²

(1)

Kirkland, WA, USA

(2)

Seattle, WA, USA

 

At regular intervals , the team ref lects on how to become more effective, then tunes and adjusts its behavior accordingly.—agilemanifesto.org/principles

Looking Back

We’ve talked about several mechanisms in this book for looking back and reflecting on past progress with an aim to becoming more effective as individuals, managers, and teams. To recap those mechanisms, they include Retrospectives, Data Wallows, Quality Reviews, Live Site Reviews, Engineering Reviews, and Surveys.

Retrospectives as discussed in Chapter 4: Aligning with ...