Applied Machine Learning: a Netflix Production, Deep Recommendations at Twitch

This is a megameetup hosted by Twitch! The hosts present their tech as well as the talks from Netflix and Aperture Data engineers. Thank you so much Twitch! This meetup will be twitched -- expect a link shortly!

(1) Applied Machine Learning is about as mature as Software Engineering circa 1998. For Data Scientists, it’s hard to collaborate, hard to be productive and hard to deploy to production. In the last 20 years, Software Engineers have become far more collaborative thanks to tools like git, far more productive thanks to cloud computing and far more effective at delivering quality software thanks to CI/CD and agile development practices. At Netflix, I get to work on problems like: how do we scale Data Science innovation by making collaboration effortless? How do we enable Data Scientists to single-handedly and reliably introduce their models to production? How do we make it easy to develop ML models that humans trust? More importantly, how do we use ML to make humans BETTER? In this talk, we’ll explore how Netflix is approaching these problems to further our mission of creating joy for our 125 Million+ members worldwide!

Speaker: Julie Pitt leads the Machine Learning Infrastructure at Netflix, with the goal of scaling Data Science while increasing innovation. She previously built streaming infrastructure behind the "play" button while Netflix was transitioning from domestic DVD-by-mail service to international streaming service.

Julie also co-founded Order of Magnitude Labs, with a mission to build AI capable of doing things that humans find easy and today’s machines find hard: exploration, communication, creativity and accomplishing long-range goals. Early in her career, Julie developed data processing software at Lawrence Livermore National Laboratory that enabled scientists to study the newly-sequenced human genome.

(2) Deep Recommendations at Twitch
Abstract: Deep Recommendations at Twitch: Twitch is a social video platform that democratizes broadcasting, with 15 million + daily viewers. In this talk we'll explore some of the difficulties that live content introduces to recommendations, and the recommender we built to personalize many products at Twitch. In particular, we'll explore some of the architecture decisions we made and what informed them. We'll also discuss some of our learnings around offline metrics and things to keep an eye on as you move to online experiments.

Speaker: Mark Ally is a Senior Applied Scientist at Twitch, working on deep learning techniques for recommendation systems

(3) Let Us Manage Your Visual Data So You Can Make Machines Learn Better

ApertureData's platform accelerates AI applications through its Data Management solution that redefines how large visual data sets are stored, searched and processed. It exposes a unified interface that allows users to store and search both the data and metadata associated with visual artifacts (images or videos). ApertureData's platform provides several innovative features: the ability to evolve metadata easily without requiring costly schema change, first-class status for feature vectors and bounding boxes, the ability to perform similarity searches as well as the ability to perform common pre-processing operations close to the data. The platform will be pluggable in allowing data to be stored on different backends and serve any machine learning pipeline.

Speaker: Vishakha Gupta is the Founder and CEO at ApertureData. Prior to that, she was at Intel Labs for over 7 years where she led the design and development of VDMS (the Visual Data Management System) which forms the core of ApertureData's platform. Vishakha graduated from the Georgia Institute of Technology with a Ph.D in Computer Science where her work focused on virtualization.

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Julie is a regular speaker at Scale By the Bay, the 2019 CFP opens May 1 and ends May 31, submit your best talks early starting May 1 at http://scale.bythebay.io!