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End-to-end demand forecasting and retraining workflow

Data Scientist
Data Scientist

End-to-end demand forecasting and retraining workflow

Author - Andrii Kruchko


This accelerator  demonstrates retraining policies with DataRobot MLOps demand forecast deployments.


This accelerator is a another installment of a series on demand forecasting. The first accelerator focuses on handling common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation. The second accelerator provides the building blocks for cold start modeling workflow on series with limited or no history. They can be used as a starting point to create a model deployment for the app. The third accelerator is a what-if app that allows users to adjust certain known in advance variable values to see how changes in those factors might affect the forecasted demand.


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