Interview with Sr. Director of Product at H2O.ai: Patrick Hall | Machine Learning, H2O.ai & Machine Learning Interpretability

Chai Time Data Science Playlist: https://www.youtube.com/playlist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x In this episode, Sanyam Bhutani interviews Patrick Hall, Sr. Director of Product at H2O.ai. Patrick has a background in Math and has completed a MS Course in Analytics. In this interview they talk all about Patrick's journey into ML, ML Interpretability and his journey at H2O.ai, how his work has evolved over the years. They talk a lot about MLI, ML Explainability and Model Debugging. They also talk about how these ideas are implemented inside of h2o.ai and how can someone bring these ideas to their pipelines. Links: "Real-World Strategies for Model Debugging": https://medium.com/@jphall_22520/strategies-for-model-debugging-aa822f1097ce An Intro to MLI Book: https://www.h2o.ai/wp-content/uploads/2019/08/An-Introduction-to-Machine-Learning-Interpretability-Second-Edition.pdf "Why you should care about debugging machine learning models": https://www.oreilly.com/radar/why-you-should-care-about-debugging-machine-learning-models/ "Proposed Guidelines for the Responsible Use of Explainable Machine Learning": https://arxiv.org/pdf/1906.03533.pdf Follow: Patrick Hall: https://twitter.com/jpatrickhall https://www.linkedin.com/in/jpatrickhall/ Sanyam Bhutani: https://twitter.com/bhutanisanyam1 Blog: sanyambhutani.com About: http://chaitimedatascience.com/ A show for Interviews with Practitioners, Kagglers & Researchers and all things Data Science hosted by Sanyam Bhutani. You can expect weekly episodes every available as Video, Podcast, and blogposts. Flow by LiQWYD https://soundcloud.com/liqwyd

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