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

arXiv: https://arxiv.org/abs/2304.10660 · HTML: https://mlsanity.com/paper/2304.10660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Improving dermatology classifiers across populations using images generated by large diffusion models](https://mlsanity.com/paper/2211.13352/) (2022)
- [Model Complexity-Accuracy Trade-off for a Convolutional Neural Network](https://mlsanity.com/paper/1705.03338/) (2017)

## Abstract (arXiv)

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.