arkid

I am a big fan of Ronny Kohavi. His book “Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing” should literally be considered a bible for any startup trying to scale up on their experimentation. In this post, I don’t want to give a summary of the book in any way. I am, however, going to give some complimentary points which might help teams which are planning to scale up on experimentation.

Practical advises on recommendation systems in production

ML team productivity, hiring, skillsets, motivation etc

I have spent a considerable amount of time in various organizations as part of their data teams. During this while, I had the opportunity to spend a lot of time with data people [Analytics, Decision Sciences, Analytics Engineers, Data Scientists, Applied Scientists, Machine Learning Engineers, Data Engineers, Data Analysts etc]. I also had the opportunity to work with senior leaders in the technology space and otherwise. Today, while thinking through what makes or breaks an org from being data driven, I thought I would jot down some of the obvious pointers.