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arXiv · 2304.10660

Scaling ML Products At Startups: A Practitioner's Guide

Atul Dhingra·Gaurav Sood
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Abstract

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.

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Atul Dhingra, Gaurav Sood. 2023-04-20. Scaling ML Products At Startups: A Practitioner's Guide. https://arxiv.org/abs/2304.10660

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