As machine learning models are becoming more common in production, organizations are recognizing the significance of continuous validation, and are integrating automated testing into their CI/CD pipelines to ensure that their models remain relevant and are trustworthy. However, with constantly changing data and black-box logic, testing these models can be a daunting task.
In this talk, we'll explore the common pitfalls of ML models and best practices for testing them. We'll demonstrate how to use the deepchecks open source package to validate models and data during the research and CI/CD phases . By the end of the session, attendees will be equipped with the knowledge and tools to integrate ML model testing into their workflow, ensuring their models are reliable and maintainable.
Shir is the co-founder and CTO of Deepchecks, an MLOps startup for continuous validation of ML models and data. Previously, Shir worked at the Prime Minister’s Office and at Unit 8200, conducting and leading research in various Machine Learning and Cybersecurity related challenges. 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 was selected as a featured honoree in the Forbes Europe 30 under 30 class of 2021.