In this conversation, James shares some hard-won lessons on how to effectively use technology to create applications powered by machine learning models.

// 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!