Machine Learning (and deep learning) applications are quickly gaining in popularity, but keeping the process agile by continuously improving it is getting more and more complex. There are many reasons for this, but primarily, behaviors are complex and difficult to anticipate, making them resistant to proper testing, harder to explain, and thus not easy to improve.

Building a healthy productive engineering culture is hard and just deploying frequently with continuous deployment is not enough. I'm going to show you how you can use a carrot, not a stick, approach to take continuous deployment to the next level by celebrating team values with Sleuth.