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Building for Small Data Science Teams // James Lamb // MLOps Coffee Sessions #69

Dec 20, 2021

52:36

Creator website

Highlights

AI Chapters

Episode notes

Data Science and Data Engineering

05:08

The Trade-Off Between Speed and Confidence

05:46

Is There a Use Case for Mtred Models?

02:11

Introduction

4min

The Biggest Innovation in Tree Base Surveillance

2min

Licebum

3min

Gb M - What's the Community Like?

5min

A New Approach to Data Science Development

2min

Is That Where We're at Right Now?

4min

Is There a Use Case for Mtred Models?

2min

The Trade-Off Between Speed and Confidence

6min

Is There a Tool Out There That I'm Really Excited About?

3min

10.

Are You Considering Airflow for Machine Learning?

4min

11.

Design Decisions for Data Science Teams

3min

12.

Data Science and Data Engineering

5min

13.

Machine Learning and Data Engineering - The Definition of a Team

2min

14.

I'm a Data Scientist, and I'm Proud of the Results of That Work

3min

15.

Are You Hiring Machine Learning Engineers?

2min

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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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka/
Connect with James on LinkedIn: https://www.linkedin.com/in/jameslamb1/

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!