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Key takeaways
- Sanyam Bhutani interviewed Arno Candel, CTO at H2O, about his journey from physics to software engineering to machine learning.
- The interview with Arno Candel covers H2O's Maker Culture and discusses both H2O's open source and commercial products.
- The show notes for the Arno Candel interview include a segment on AutoML versus keeping a human in the loop, timestamped at 39:10.
- The interview also covers Arno Candel's Kaggle competition experience and how it compares to working as a team at H2O.
In this episode, Sanyam Bhutani interviews Dr. Arno Candel: CTO at H2O.ai
They talk about Arno’s journey into the field with amazing comments and insights by Arno applicable to the field.
They talk all about Arno’s journey and ML, Automated Machine Learning Broadly speaking. Arno’s journey from Physics to Software Engineering to Machine Learning Broadly speaking, his journey at H2O.
They also discuss H2O’s products, vision and The “Maker Culture”. You can watch the interview below:
Or if you prefer to tuning in to the audio version, you can find it here:
Links:
Follow:
- Arno Candel:
- H2O.AI:
- Sanyam Bhutani:
Show Notes/Timings
- 2:28 When did you find your passion for the field?
- 9:07 Startup Life-Pulled into ML
- 13:35 Do you find any parallels in terms of the world of Physics and Data Science?
- 18:25 What does a day in your life look like?
- 27:53 “Maker Culture” at H2O
- 33:45 H2O.ai has a lot “products”: Both Open Source and Commercial. Could you help us understand about these? Tell us about the ones you work on?
- 39:10 AutoML or Human in the loop?
- 45:15 Journey as the CTO and the growth of products
- 55:08 Coming to Kaggle. You’re a comp Master. Could you tell us what made you sign up for your first competition? How did you get started on Kaggle?
- 57:15 Can you tell us if you’ve learned more as a team on Kaggle vs as a team at H2O.ai?
- 1:00:05 Can you tell us about your Kaggle experience? How has what you have learnt taking part in Kaggle competitions impacted your professional life?
- 1:02:10 Software Engineering practises and Data Science Practises. Where do we draw the line?
- 1:04:40 Best advice do you have for someone just starting with ML