Last week we hosted Machine Learning @Scale, bringing together data scientists, engineers, and researchers to discuss the range of technical challenges in large-scale applied machine learning solutions.
More than 300 attendees gathered in Manhattan's Metropolitan West to hear from engineering leaders at Bloomberg, Clarifai, Facebook, Google, Instagram, LinkedIn, and ZocDoc, who all shared diverse approaches to building machine learning systems used by millions if not billions of people.For a recap of the conference and the presentations, check out the videos below. If you're interested in joining the next event, visit the @Scale website or join the @Scale community.
Joaquin Candela, Facebook
Joaquin shares insight into how Facebook is conducting and applying industry-leading research to help drive advancements in AI disciplines like computer vision and language understanding.
Parth Vasa, Bloomberg
Parth discusses the challenge of providing effective search for financial markets, balancing the need for accuracy and speed, the diversity of the data, and the difficulty of gaining an accurate picture of markets from moment to moment. He describes the ways in which machine learning is helping Bloomberg to address this challenge.
Xiaoqiang Luo, LinkedIn
Xiaoqiang presents a recent project in which papers, patents, and other professional content created by LinkedIn members are pulled from the web and matched to their creators automatically. The matched content is sent to LinkedIn members as a notification on LinkedIn's mobile platform, providing people with an opportunity to add content to their profiles with ease.
Andres Munoz Medina, Google
Andres describes the challenges of learning in repeated auctions for revenue maximization.
Thomas Dimson, Instagram
Thomas uses the launch of Instagram's feed ranking as a working example to talk through issues in quantifying network effects, while exploring unusual A/B testing techniques such as country-level tests, testing on balanced graph partitions, and author-side experiments.
Danielle Rothermel and Jan Kodovsky, Facebook
Jan and Danielle offer a deep dive into a system capable of interpreting location signals coming from mobile devices at scale. The case study they present exposes challenges their team faced while designing and productionizing a system that understands people's spatio-temporal movements in a physical world and powers a series of location-aware products at Facebook.
Michelle Ye, ZocDoc
Michelle Ye uses her perspective as a data scientist to talk through the challenge of scaling engineering ideas through an organization to gain buy-in and bring benefits to both the company and the end user.
Matthew Zeiler, Clarifai
Clarifai CEO Matthew Zeiler takes viewers through a suite of product demos for identifying, classifying, and searching digital images using the company's machine learning technology.
Many thanks to all the speakers who presented and to all the attendees. We look forward to more opportunities to interact with the New York machine learning community in the future!