https://github.com/soodoku/scaling

Scaling ML Products At Startups: A Practitioner's Guide
https://github.com/soodoku/scaling

Last synced: 13 days ago
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Scaling ML Products At Startups: A Practitioner's Guide

• Host: GitHub
• URL: https://github.com/soodoku/scaling
• Owner: soodoku
• Created: 2023-03-26T22:34:17.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-04-20T21:58:22.000Z (over 1 year ago)
• Last Synced: 2024-10-11T12:15:44.966Z (27 days ago)
• Language: TeX
• Homepage:
• Size: 121 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Scaling ML Products At Startups: A Practitioner's Guide

How do you scale a machine learning product at a startup? In particular, how do you serve a greater volume, velocity, and variety of queries cost-effectively? We break down costs into variable costs—the cost of serving the model and performant—and fixed costs—the cost of developing and training new models. We propose a framework for conceptualizing these costs, breaking them into finer categories, and limn ways to reduce costs. Lastly, since in our experience, the most expensive fixed cost of a machine learning system is the cost of identifying the root causes of failures and driving continuous improvement, we present a way to conceptualize the issues and share our methodology for the same.

## Manuscript

* [ms](ms/)

## Authors

Atul Dhingra and Gaurav Sood