Rustling up predictive sporting betting models on the BEAM

Rustler is a library that makes easy to bind Rust code to the BEAM as a NIF. At SimpleBet, David and his team took that to the next level by implementing our machine learning models in Rust as an application-level NIF. Affectionately referred to as the Dream Stack, they've used this approach to build a system that generates the odds of every plate appearance outcome at a baseball game. While it worked well to start, they are now migrating to a service-based approach instead.

THIS TALK IN THREE WORDS

Very

Big

NIF

OBJECTIVES

Teach the audience how/why to write NIFs using Rustler Demonstrate SimpleBet's approach to using NIFs and how we took it too far Explain why that approach will no longer work well for SimpleBet and why moving to a service-based approach, while slower, aligns much better with our company goals

TARGET AUDIENCE

The audience for this talk are those who are interested in Rust and machine-learning, but most importantly, those who want to learn from about an architecture that worked really well until it didn't, and how to migrate away from that type of situation.