I had originally intended to cover a wide variety of general "gotchas" around the practical applications of machine learning, however with half an hour there's really only so much you can cover.
The talk ended up being more of an overview of binary classification, as well as some anecdotes around mistakes in using machine learning I've actually seen in the field, including:
- Not doing any model evaluation at all
- Doing model evaluation but without cross-validation
- Not knowing what the cold start problem is and how to avoid it with a recommender system
All in all it was received very well despite being review for a lot of people in the room. As usual, I took away some learnings around presenting:
- Always lowball for time (the presentation was rushed despite my blistering pace)
- Never try to use fancy fonts in Powerpoint and expect them to carry over - it never works (copy paste as an image instead when you've got the final presentation)
Dan Thierl of Rubikloud gave a really informative and candid talk about what product management at a data science startup can look like. In particular, I was struck by his honesty around the challenges faced (both from technical standpoint and with clients), how quickly you have to move / pivot, and how some clients are just looking for simple solutions (Can you help us dashboard?) and are perhaps not at a level of maturity to want or fully utilize a data science solution.
All in all, another great meetup that prompted some really interesting discussion afterward. I look forward to the next one. I've added the presentation to the speaking section.