---
title: Demand forecasting and retraining workflow
description: Implement retraining policies with DataRobot MLOps demand forecast deployments.

---

# Demand forecasting and retraining workflow {: #demand-forecasting-and-retraining-workflow }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/Demand_forecasting3_retraining/End_to_end_demand_forecasting_retraining.ipynb){ .md-button }

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](demand-flow) 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](cold-start) 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](ml-what-if) is a what-if app that allows you to adjust certain known in advance variable values to see how changes in those factors might affect the forecasted demand.
