This track will talk about the practice of moving machine learning systems from development to deployment, and the next generation tooling that makes this an organic process. Removing barriers to deployment allow data scientists to gain quicker feedback and deeper insights into how models are performing in the wild, and enable nimbler experimentation at the product level.To do this, new machine learning workflows are taking advantage of serverless and container orchestration technologies, and also specializing frameworks towards certain classes of data science problems.This track is for the hands on practitioners who fluidly cross the boundary between research and deployment.

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Masha Danilenko

Engineering Lead | Insight Data Science

Masha Danilenko is currently leading a team at Insight Data Science as an Engineering Lead. A pure mathematician by training with a Master's from Moscow State University, she transitioned from academia to industry and worked as a System Analyst Lead prior to moving to the US. After the move, she became a Data Engineering Fellow at Insight and then joined Capital One Labs as a Data Engineer.

As part of her current role, she has helped over a hundred professionals transition into Data and DevOps Engineering roles across North America. She and her 100% women engineering team are providing Insight Fellows with hands-on experience in cutting edge technologies at a 7-week intensive program in New York.

Masha Danilenko

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