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.
翻译：在初创公司如何扩展机器学习产品？特别是，在成本效益的前提下，如何服务更大量、更快速、更多样化的查询？我们将成本分为可变成本——提供模型服务的成本和高性能成本——和固定成本——开发和训练新模型的成本。我们提出了一个框架来概念化这些成本，将其细分为更精细的类别，并介绍减少成本的方法。最后，由于根本原因的识别和持续改进是机器学习系统最昂贵的固定成本，因此我们提出了一种概念化问题的方法，并分享了我们的方法。
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