The customer’s existing ML models determining Pricing and Promotion had high infrastructure cost, slow run time, high maintenance, low prediction quality, and could not be migrated to multiple geographies. Furthermore, training the model for any enhancements was expensive and time-consuming. Sigmoid used MLOps to solve the challenges that brought scalability and enabled

the migration of the model across geographies to create a unified approach. It also reduced the model run time drastically while improving the prediction’s MAPE (Mean Absolute Percentage Error) by 20%.

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