ABOUT THE TALK 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 explore the common pitfalls of ML models and best practices for testing them. We demonstrate how to use the deepchecks open source package to validate models and data during the research and CI/CD phases.

ABOUT THE SPEAKER Shir Chorev 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.

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