Lessons learned from doing data science, at scale, in industry
Linear Digressions
English - November 25, 2019 00:45 - 28 minutes - 12.8 MB - ★★★★★ - 350 ratingsTechnology data science machine learning linear digressions Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
If you’ve taken a machine learning class, or read up on A/B tests, you likely have a decent grounding in the theoretical pillars of data science. But if you’re in a position to have actually built lots of models or run lots of experiments, there’s almost certainly a bunch of extra “street smarts” insights you’ve had that go beyond the “books smarts” of more academic studies. The data scientists at Booking.com, who run build models and experiments constantly, have written a paper that bridges the gap and talks about what non-obvious things they’ve learned from that practice. In this episode we read and digest that paper, talking through the gotchas that they don’t always teach in a classroom but that make data science tricky and interesting in the real world.
Relevant links:
https://www.kdd.org/kdd2019/accepted-papers/view/150-successful-machine-learning-models-6-lessons-learned-at-booking.com