LyftLearn is Lyft’s ML Platform. It is a machine learning infrastructure built on top of Kubernetes that powers diverse applications such as dispatch, pricing, ETAs, fraud detection, and support. In a previous blog post, we explored the architecture and challenges of the platform.
In this post, we will focus on how we utilize the compute layer of LyftLearn to profile model features and predictions and perform anomaly detection at scale.
whylogs author here - thank you for the shout out.
For those who don’t know, we are building whylogs to be the equivalent of opentelemetry for ML and Data Ops. It’s designed to be lightweight and scalable, as discussed in this blog post!
+1 as another whylogs author here. I really appreciate the description of why characteristic like mergeability and serialization efficiency are important to these large scale use cases.
In this post, we will focus on how we utilize the compute layer of LyftLearn to profile model features and predictions and perform anomaly detection at scale.