Last Updated on September 5, 2016 In a recent presentation at MLConf, Xavier Amatriain described 10 lessons that he has learned about building machine learning systems as the Research/Engineering Manager at Netflix. In this you will discover these 10 lessons in a summary from his talk and slides. Lessons Learned from Building Machine Learning Systems Taken from Xavier’s presentation 10 Lessons Learned The 10 lessons that Xavier presents can be summarized as follows: More data vs./and Better Models You might […]

Last Updated on August 15, 2020 In a 2010 post Hilary Mason and Chris Wiggins described the OSEMN process as a taxonomy of tasks that a data scientist should feel comfortable working on. The title of the post was “A Taxonomy of Data Science” on the now defunct dataists blog. This process has also been used as the structure of a recent book, specifically “Data Science at the Command Line: Facing the Future with Time-Tested Tools” by Jeroen Janssens published […]

Last Updated on June 7, 2016 In a recent presentation, Ben Hamner described the common pitfalls in machine learning projects he and his colleagues have observed during competitions on Kaggle. The talk was titled “Machine Learning Gremlins” and was presented in February 2014 at Strata. In this post we take a look at the pitfalls from Ben’s talk, what they look like and how to avoid them. Machine Learning Process Early in the talk, Ben presented a snap-shot of the process for working […]

Last Updated on June 7, 2016 There was a recent question that asked “How to not waste-time/procrastinate while ml scripts are running?“. I think this is an important question. I think answers to this question show a level of organization or maturity in your approach to work. I left a small comment on this question, but in this post I elaborate on my answer and give you a few perspectives on how to consider this question, minimize it and even […]