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How to effectively test and debug machine learning models, from ML engineer@Apple to startup founder - Gabriel Bayomi - the data scientist show #055
Gabriel Bayomi is the Co-Founder at OpenLayer, a tool that tests & debugs machine learning models. OpenLayer was in the YCombinator’s batch in 2021, building tools for machine learning model testing. Previously he was a machine learning engineer at Apple working on Siri. He has a master degree in computer science from Carnegie Mellon. He is passionate about Natural Language Processing, Machine Learning, and Computational Social Science. We talked about how to test and debug machine learning models, his experience at Apple, and career lessons. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science and career.
Gabriel’s LinkedIn: https://www.linkedin.com/in/gbayomi
Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/
Daliana's Twitter: https://twitter.com/DalianaLiu
(0:00) Intro
(01:01:39) How he got into machine learning
(01:06:43) His experience at Apple, Siri
(01:15:55) How to validate the solution
(01:19:39) Benefits of using external error analysis framework
(01:21:30) How to build a model evaluation pipeline
(01:28:26) Don’t overfit the subset of data
(01:33:19) Your validation set shouldn’t be fixed
(01:41:03) Become one with data
(01:44:05) Three model interpretability library you should use
(01:50:47) Common mistakes people made in model validation
(01:53:33) How to create an adversarial test
(01:55:43) How to check data quality
(01:06:46) Transition from engineer to executive
(01:10:04) Things he learnt from his favorite coworker
(01:17:57) how job roles would evolve
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By Daliana Liu
4.7
7575 ratings
Gabriel Bayomi is the Co-Founder at OpenLayer, a tool that tests & debugs machine learning models. OpenLayer was in the YCombinator’s batch in 2021, building tools for machine learning model testing. Previously he was a machine learning engineer at Apple working on Siri. He has a master degree in computer science from Carnegie Mellon. He is passionate about Natural Language Processing, Machine Learning, and Computational Social Science. We talked about how to test and debug machine learning models, his experience at Apple, and career lessons. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science and career.
Gabriel’s LinkedIn: https://www.linkedin.com/in/gbayomi
Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/
Daliana's Twitter: https://twitter.com/DalianaLiu
(0:00) Intro
(01:01:39) How he got into machine learning
(01:06:43) His experience at Apple, Siri
(01:15:55) How to validate the solution
(01:19:39) Benefits of using external error analysis framework
(01:21:30) How to build a model evaluation pipeline
(01:28:26) Don’t overfit the subset of data
(01:33:19) Your validation set shouldn’t be fixed
(01:41:03) Become one with data
(01:44:05) Three model interpretability library you should use
(01:50:47) Common mistakes people made in model validation
(01:53:33) How to create an adversarial test
(01:55:43) How to check data quality
(01:06:46) Transition from engineer to executive
(01:10:04) Things he learnt from his favorite coworker
(01:17:57) how job roles would evolve
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