Lyft is a ride-sharing company which is a two-sided marketplace; balancing supply and demand using various levers (passenger pricing, driver incentive etc.) to maintain an efficient system. Lyft has built a real-time optimization platform that helps to build the product faster. This complex system makes real-time decisions using various data sources; machine learning models; and a streaming infrastructure for low latency, reliability and scalability. This infrastructure consumes a massive number of events from different sources to make real-time product decisions.
In this session, we will discuss how Lyft organically evolved and scaled the streaming platform that provides a consistent view of the marketplace to aid an individual team independently run their optimization. The platform offers online and offline feature access that helps teams to back test their model in the future. It provides various other powerful capabilities such as replaying the production ML feature in PyNotebook, feature validation, near real-time model training, executing multi-layer of models in a DAG, etc. The speaker will elaborate things that helped him scale the systems to process millions of events per minute and power T0 products with tighter latency SLA.
Add comment