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title: Re-imagining ML Serving Infra: My Winter Internship at Ramp
description: An intern on Ramp&#x27;s ML Platform team describes building a dedicated model serving layer called mw-serve during a 14-week winter internship. The project aimed...
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og:description: An intern on Ramp&#x27;s ML Platform team describes building a dedicated model serving layer called mw-serve during a 14-week winter internship. The project aimed...
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# Re-imagining ML Serving Infra: My Winter Internship at Ramp

**[Ramp Engineering](https://daily.dev/sources/ramp_eng)** · 6 min read · 0 upvotes · 0 comments

## Summary

An intern on Ramp's ML Platform team describes building a dedicated model serving layer called mw-serve during a 14-week winter internship. The project aimed to decouple ML model serving from the intelligence platform (intel plat), a high-uptime service processing ~10M requests/week for fraud detection and credit underwriting. The solution containerizes models with their training environments using ECR and MLflow, enabling dependency isolation. Key engineering challenges included meeting a ~15ms latency budget for card transaction fraud detection, diagnosing tail latencies caused by Python GC pauses blocking the GIL, and adding observability to each component of the inference pipeline. By the end, one model was serving in production with others in shadow mode.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://builders.ramp.com/post/re-imagining-ml-serving-infra>

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- [Day 96: AI Model Serving — Taking AI From Research to Production](https://daily.dev/posts/day-96-ai-model-serving-taking-ai-from-research-to-production-zulmnlgzb) · Medium · 0 upvotes · 0 comments

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Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#containers](https://daily.dev/tags/containers)

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