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When you’re training machine learning models in a lab, the goal is to optimize the model for a specific metric. When you’re building real-world ML applications, you must take a broader view of the application, the economics, the end users, and the market to make the right decision.

In this post, I’ll share a hard-earned lesson in adjusting the economics of machine learning products from my experience. This can help you avoid similar mistakes and get your ML products working faster.