
Why Your ML Model Requires a Product Approach: A Case Study
In the field of machine learning, many teams face a critical challenge: developing models that, despite achieving high levels of accuracy during validation phases, fail to generate business results once implemented. This phenomenon has been evident in situations where, after achieving a 94% accuracy rate in the validation set, a recommendation engine has not been able to drive the expected











