Machine Learning Street Talk (MLST) Kaggle, ML Community / Engineering (Sanyam Bhutani)
Oct 28, 2020
Sanyam Bhutani, a prominent machine learning engineer and AI content creator at H2O, dives into the world of data science and the Kaggle community. He shares the importance of self-directed learning versus formal education in ML, offering insights from his own journey. Sanyam discusses the challenges of transitioning Kaggle models to real-world applications and highlights the necessity of engineering rigor in ML practices. He also emphasizes building authentic professional connections and the significance of model interpretability in high-stakes situations.
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Unconventional Path to ML
- Sanyam Bhutani's entry into ML was driven by curiosity after mentors discouraged him from pursuing it.
- He learned online and prioritized his interests, leading to diverse content creation and community involvement.
Content Creation's Value
- Content creators bridge the gap between complex research papers and the public's understanding.
- They simplify complex information, making cutting-edge technology accessible to a wider audience.
Community-Taught Learning
- Formal education offers community, but online resources and communities can replace traditional learning.
- Sanyam considers himself "community taught", emphasizing learning through forums, videos, and podcasts.