Uploaded December 2022 | Updated September 2026, 1 week ago
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 dalianaliu.com for more on data science and career.
Daliana's Twitter: twitter.com/DalianaLiu
Daliana's LinkedIn: linkedin.com/in/dalianaliu
Philip’s LinkedIn: linkedin.com/in/philip-tannor-a6a910b7/?originalSubdomain=il
Augboost: 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
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 dalianaliu.com for more on data science and career.
Daliana's Twitter: twitter.com/DalianaLiu
Daliana's LinkedIn: linkedin.com/in/dalianaliu
Philip’s LinkedIn: linkedin.com/in/philip-tannor-a6a910b7/?originalSubdomain=il
Augboost: 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
