Sagemaker: Training everytime I need make a prediction: how should I structure the solution?

I have been asked to migrate a custom model to Sagemaker. This model is a forecasting script that trains everytime it is run and then predicts after training. (It is a two-layer forecasting prediction with SARIMAX). The flow is as explained below:

  1. train arima model to get exogenous variables (training algorithm 1)
  2. predict with that trained model
  3. use the output variables to train the second layer (training algorithm 2)
  4. predict with this last trained model and output the solution

This is not what im used to do in Sagemaker (I train a model once that will be invoked multiple times), so how could I frame this? Train models separately from two separate docker images and create two endpoints? The whole train-predict-train-predict workflow would no longer be automatic, right? How would I trigger this workflow? Please help!