apply(risk)
Virtual Conference | Tuesday, May 30 | 9:30 am – 3:00 pm PT
apply(risk) is an ML engineering community conference where practitioners will share best practices and architectures of choice for building risk and fraud detection systems. Join us to learn from and connect with your peers!
Featured Speakers
Louis Brandy
VP Engineering
Bio
Cooper Stimson
Software Engineer, Machine Learning Platform
Francisco Arceo
Engineering Manager
Aravind Maguluri
Lead Data Scientist
Jake Weholt
Engineering Manager, Machine Learning - Fraud Detection
Mike Del Balso
CEO
Bio
Tocho Tochev
Lead ML Engineer
Dat Ngo
Data Scientist and ML Engineer
Bio
Devvret Rishi
Co-founder and Chief Product Officer
Bio
Vincent Houdebine
Senior Solutions Architect
Bio
Demetrios Brinkmann
Founder
Bio
Louis Brandy
VP Engineering, Rockset
Louis Brandy is the Vice President of Engineering at Rockset. Prior to Rockset, Louis was Director of Engineering at Facebook. During his time there, he was an early engineer and manager in Facebook’s Site Integrity organization where his team built much of the anti-abuse infrastructure that powers Facebook’s spam fighting, fraud detection, and other online, real-time classification systems. He also worked on Facebook’s RPC and service discovery ecosystem and built and supported the C++ infrastructure teams responsible for the overall health of the Facebook C++ codebase, working on compilers, sanitizers, linters, and core (and open-source) libraries like folly, jemalloc, and fbthrift.
Sponsors
Previous Speakers
Yuchen Wu
VP Engineering
Smitha Shyam
Director of Engineering
Tzvetelina Tzeneva
Staff ML Engineer
Lex Beattie
Machine Learning Engagement Lead
Yuyang (Rand) Xie
Senior Machine Learning Engineer
Ravi Kiran Chirravuri
Software Engineer
Katrina Ni
Machine Learning Engineer
Emmanuel Ameisen
Staff Machine Learning Engineer
Joshua Hansen
ML Platform Tech Lead
The ML Data Engineering Community Conference Series
apply() is a community conference series for machine learning and data teams to discuss the practical data engineering challenges faced when building real-time machine learning systems. Participants learn from industry experts and share best practices with the community.
5
Previous Events
21,000+
Registrants
98
Sessions
123
Speakers
1
Live Haircut
Interested in speaking at a future apply() event?
The community loves hearing from machine learning and data practitioners about their journey to operationalizing ML.
