Experimentation culture of Netflix

Machine Learning is defined by a need for rapid experimentation. To achieve an environment of fast, iterative and low-risk experimentation, both hard aspects (tools and platforms) and soft aspects (culture, ways of working) of the ecosystem need to be aligned. Ville Tuulos, the driving force behind Netflix’s Metaflow platform, explains how Netflix has managed to tackle both sides of the coin to build a truly experimentation-oriented organization.

Ville is among the most renowned of Finnish data scientists. Spinning out of self-organising map research in Finland, he quickly landed in Silicon Valley at the first dawn of AI market emergence, focusing on the development and implemenation of deployment infrastructures. This led him to push platform development at Netflix, which later became Metaflow, Netflix’s Open Source model design and deployment framework. Today, he’s spinning out a development platform company, and will be releasing his book, Effective Data Science Infrastructure, this spring.

Tune in to hear Ville’s point of view on the how a culture of freedom and responsibility is created, the role of processes (or lack thereof), and how the bridge between a thousands of great ideas for ML application should be validated and proven. Get Ville’s predictions on the future trends of ML management and human-machine collaboration.

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