Building Real-time Machine Learning Foundations at Lyft

In early 2022, Lyft already had a comprehensive Machine Learning Platform called LyftLearn composed of <a href="https://eng.lyft.com/powering-millions-of-real-time-decisions-with-lyftlearn-serving-9bb1f73318dc" rel="noopener ugc nofollow" target="_blank">model serving</a>, <a href="https://eng.lyft.com/lyftlearn-ml-model-training-infrastructure-built-on-kubernetes-aef8218842bb" rel="noopener ugc nofollow" target="_blank">training</a>, CI/CD, <a href="https://eng.lyft.com/ml-feature-serving-infrastructure-at-lyft-d30bf2d3c32a" rel="noopener ugc nofollow" target="_blank">feature serving</a>, and <a href="https://eng.lyft.com/full-spectrum-ml-model-monitoring-at-lyft-a4cdaf828e8f" rel="noopener ugc nofollow" target="_blank">model monitoring</a> systems. On the real-time front, LyftLearn supported real-time inference and input feature validation. However, streaming data was not supported as a first-class citizen across many of the platform’s systems — such as training, complex monitoring, and others. While several teams were using streaming data in their Machine Learning (ML) workflows, doing so was a laborious process, sometimes requiring weeks or months of engineering effort. On the flip side, there was a substantial appetite to build real-time ML systems from developers at Lyft. Lyft is a real-time marketplace and many teams benefit from enhancing their machine learning models with real-time signals. To meet the needs of our customers, we kicked off the <code>Real-time Machine Learning with Streaming</code> initiative. Our goal was to develop foundations that would enable the hundreds of ML developers at Lyft to efficiently develop new models and enhance existing models with streaming data. In this blog post, we will discuss some what we built in support of that goal and the lessons we learned along the way. <a href="https://eng.lyft.com/building-real-time-machine-learning-foundations-at-lyft-6dd99b385a4e">Learn More</a>