"Lyft has rearchitected its machine learning platform LyftLearn into a hybrid system, moving offline workloads to AWS SageMaker while retaining Kubernetes for online model serving. Its decision to choose managed services where operational complexity was highest, while maintaining custom infrastructure where control mattered most, offers a pragmatic alternative to unified platform strategies. Lyft's engineers migrated LyftLearn Compute, which manages training and batch processing, to AWS SageMaker, eliminating background watcher services, cluster autoscaling challenges, and eventually-consistent state management, which had consumed significant engineering effort."
"We adopted SageMaker for training because managing custom batch compute infrastructure was consuming engineering capacity better spent on ML platform capabilities. We kept our serving infrastructure custom-built because it delivered the cost efficiency and control we needed. LyftLearn supports hundreds of millions of daily predictions across dispatch optimization, pricing, and fraud detection, with thousands of training jobs per day serving hundreds of data scientists and ML engineers."
Lyft rearchitected LyftLearn into a hybrid platform, moving offline training and batch compute to AWS SageMaker while retaining Kubernetes for real-time model serving. Migrating LyftLearn Compute to SageMaker eliminated custom watcher services, cluster autoscaling challenges, and eventually-consistent state management that had consumed significant engineering effort. The serving layer remained on Kubernetes for cost efficiency, performance, and tight integration with internal tooling. The platform supports hundreds of millions of daily predictions and thousands of training jobs per day for hundreds of data scientists and ML engineers. The hybrid approach assigns managed services where operational complexity was highest and preserves custom infrastructure where control and efficiency matter.
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