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Shir is the co-founder and CTO of Deepchecks, an MLOps startup for continuous validation of ML models and data. Deepchecks offers a customizable, plug & play, algorithm-based solution for testing and monitoring machine learning systems. Previously, Shir worked at the Prime Minister’s Office and Unit 8200, conducting and leading research in various Machine Learning and Cyber related problems. Shir has a B.Sc. in Physics from the Hebrew University, which she obtained as part of the Talpiot excellence program, and an M.Sc. in Electrical Engineering from Tel Aviv University.
Shir is the co-founder and CTO of Deepchecks, an MLOps startup for continuous validation of ML models and data. Deepchecks offers a customizable, plug & play, algorithm-based solution for testing and monitoring machine learning systems. Previously, Shir worked at the Prime Minister’s Office and Unit 8200, conducting and leading research in various Machine Learning and Cyber related problems. Shir has a B.Sc. in Physics from the Hebrew University, which she obtained as part of the Talpiot excellence program, and an M.Sc. in Electrical Engineering from Tel Aviv University.
Today, every piece of traditional software goes through comprehensive tests of various types before deployment, significantly reducing the risk of production faults. How can we adapt these ideas to the statistically-oriented world of ML? We’ll discuss practical tips and best practices for extensively testing and analyzing your model. This will enable you to “sign off” your model and detect errors in your data, model, and methodology before they’re out in the wild.
Today, every piece of traditional software goes through comprehensive tests of various types before deployment, significantly reducing the risk of production faults. How can we adapt these ideas to the statistically-oriented world of ML? We’ll discuss practical tips and best practices for extensively testing and analyzing your model. This will enable you to “sign off” your model and detect errors in your data, model, and methodology before they’re out in the wild.
| 8:45 | Reception |
|---|---|
| 9:30 | Opening words by WiDS TLV ambassadors Or Basson and Noah Eyal Altman |
| 9:40 | Dr. Kira Radinski - Learning to predict the future of healthcare |
| 10:10 | Prof. Yonina Eldar - Model-Based Deep Learning: Applications to Imaging and Communications |
| 10:40 | Break |
| 10:50 | Lightning talks |
| 12:20 | Lunch & Poster session |
|---|---|
| 13:20 | Roundtable session & Poster session |
| 14:05 | Roundtable closure |
| 14:20 | Break |
| 14:30 | Dr. Anna Levant - 3D Metrology: Seeing the Unseen |
| 15:00 | Aviv Ben-Arie - Counterfactual Explanations: The Future of Explainable AI? |
| 15:30 | Closing remarks |
| 15:40 | End |
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Scotty – By Nir Azoulay
Design: Sharon Geva
