MLOps Coffee Sessions #69 with James Lamb, Building for Small Data Science Teams co-hosted by Adam Sroka.
// Abstract
In this conversation, James shares some hard-won lessons on how to effectively use technology to create applications powered by machine learning models.
James also talks about how making the "right" architecture decisions is as much about org structure and hiring plans as it is about technological features.
// Bio
James Lamb is a machine learning engineer at SpotHero, a Chicago-based parking marketplace company. He is a maintainer of LightGBM, a popular machine learning framework from Microsoft Research, and has made many contributions to other open-source data science projects, including XGBoost and prefect. Prior to joining SpotHero, he worked on a managed Dask + Jupyter + Prefect service at Saturn Cloud and as an Industrial IoT Data Scientist at AWS and Uptake. Outside of work, he enjoys going to hip hop shows, watching the Celtics / Red Sox, and watching reality TV (he wouldn’t object to being called “Bravo Trash”).
// Relevant Links
James keeps track of conference and meetup talks he has given at https://github.com/jameslamb/talks#gallery. The audience for this podcast might be most interested in "Scaling LightGBM with Python and Dask" and "How Distributed LightGBM on Dask Works".
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Timestamps:
[00:00] Introduction to James Lamb
[01:11] James' background in the machine learning space
[03:24] LightGBM
[09:56] Community behind LightGBM
[13:36] Background of James in SpotHero
[20:06] Experience in Maturity Models
[22:40] Bottlenecks of tradeoffs between speed and confidence
[28:28] Tools to be excited about
[31:46] To code your own that's already out there
[36:33] Building design decisions
[39:36] Risk of the unicorn
[42:44] Cross team empathy
[47:18] Proudest technical accomplishment and/or biggest frustration less proud of lessons learned
[50:53] SpotHero is hiring!
[51:20] Wrap up
[51:53] Please like, subscribe, and you can leave a review!
