We, like many other large companies, do not stand still, but implement time-tested data models.
We recently had another team in R&D that was able to create a pipeline for training and using ML models in production. Thanks to this, we began to use ML models in combat conditions.
The guys from the Data Science team help us improve the quality of the forecast. On historical data, using the CatBoost library, they trained a gradient boosting model to predict the Last mile segment. On each prediction, our service goes to the experiment service, where it receives the model identifier, with which we go to another service, passing the identifier and data for prediction:
- restaurant information;
- information about the courier;
- the time the order was created.
Then we get the net time by which the courier should arrive at the client. This whole process looks like a black box for us, so if we want to retrain and test the model, we just need to get a new experiment ID and go with it for new predictions. In the course of a smooth transition to the forecast from the ML model, we reduced the MAE (mean absolute error) for Last mile.