![]() ![]() Initially, his inventory team manually changed the model’s predictions to correct the patterns they noticed, but eventually, the model’s predictions had become so bad that they could no longer use it. At the same time, the demand for some items was consistently being underestimated, leading to lost sales. The demand for some items was consistently being overestimated, which caused the extra items to expire. However, a year later, their numbers went down. They could finally boast to their investors that they were an AI-powered company. When the consulting firm handed the model over, his company deployed it and was very happy with its performance. The consulting firm took six months to develop the model. About two years ago, his company hired a consulting firm to develop an ML model to help them predict how many of each grocery item they’d need next week, so they could restock the items accordingly. Let’s start the note with a story I was told by an executive that many readers might be able to relate to. ![]() For the fully developed text, see the book Designing Machine Learning Systems (Chip Huyen, O’Reilly 2022). Note: This note is a work-in-progress, created for the course CS 329S: Machine Learning Systems Design (Stanford, 2022). ![]()
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