February 20th, 2020
Interview with Patrick Hall | Machine Learning, H2O.ai & Machine Learning InterpretabilityRSS Share Category: Data Science, Driverless AI, Explainable AI, Machine Learning Interpretability, Makers
By: Sanyam Bhutani
In this episode of Chai Time Data Science, 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.
“Real-World Strategies for Model Debugging”: https://medium.com/@jphall_22520/stra…
An Intro to MLI Book: https://www.h2o.ai/wp-content/uploads…
“Why you should care about debugging machine learning models”: https://www.oreilly.com/radar/why-you…
“Proposed Guidelines for the Responsible Use of Explainable Machine Learning”: https://arxiv.org/pdf/1906.03533.pdf
03:14: When did you discover your passion for ML?
07:26: Current job and a day in your life?
10:31: AutoML to replace humans or Humans in the loop?
12:12: Did you always enjoy teaching?
14:25: Journey at H2O.ai
20:43: Why is MLI important?
24:06: Is MLI ready to use? Is ML Still a black box?
32:27: At what stages should we look into MLI?
43:16: Can a creator of MLI tools also possibly introduce biases?
46:44: What interests you in research?
52:06: Best advice to someone just starting their journey into ML and just starting to explore MLI?