Philip Tannor is the Co-Founder and CEO of Deepchecks, a python package to run checks for machine learning models. Previously, he was the head of data science group at the Isreal Defense Force. He has a master's degree from Tel Aviv University in engineering, his thesis was about a new algorithm that combines neural networks with gradient-boosting decision trees. Today we’ll talk about his career journey, how to build your data science muscle memory, the algorithm he worked on, and how to check ML models. 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.
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LinkedIn: https://www.linkedin.com/in/dalianaliu/
Philip’s LinkedIn: https://www.linkedin.com/in/philip-tannor-a6a910b7/?originalSubdomain=il
Augboost: https://medium.com/@ptannor/augboost-like-xgboost-but-with-few-twists-e4df4017a5c4
(00:00:00) Introduction
(00:01:17) How did he get into ML
(00:02:52) Data science in the military
(00:08:15) How to take feedback
(00:13:24) Handling criticism
(00:15:12) What he worked on
(00:18:18) testing deployment
(00:21:28) How to build the data science muscle memory
(00:27:09) Improving the skills of data scientists
(00:30:42) His thesis in grad school
(00:36:59) Combine NN and gradient boosting
(00:40:05) Aug boost
(00:41:15)Tools he uses
(00:45:58) Deepchecks
(00:50:46) Most challenging part of building Deepchecks
(00:52:05) How can people contribute
(00:53:40) Behind the scenes
(00:56:09) Deciding how to fix or improve the model
(01:00:49) Advise for those who wanna create open-source projects
(01:04:07) Features to add for the enterprise product
(01:06:57) About his life and career right now
(01:08:27) Connect with Philip
