Wisdom From Machine Learning at Netflix

At Data By The Bay in May, we saw a great talk by Netflix’s Justin Basilico: Recommendations for Building Machine Learning Software. Justin describes some principles for effectively developing machine learning algorithms and integrating them into software products. We found ourselves nodding violently in agreement, and we wanted to recapitulate a few of his points that resonated most strongly with us, based on our experience working with data science teams in other organizations. Machine learning is iterative (slides 15-17) Justin emphasizes that “developing models is iterative” and experimentation is important. He also suggests “avoiding dual implementations” so it’s easy to use a model in production once it’s been built, without a re-implementation step. Domino enables experimentation and productionization in the same platform, so you can iteratively develop your models and…

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