Setting up a batch inference job

The code required for this section is in the pystock-inference-api folder. The MLflow infrastructure is provided in the Docker image accompanying the code as shown in the following figure:

Figure 9.1 – Layout of a batch scoring deployment

If you have direct access to the artifacts, you can do the following. The code is available under the pystock-inference-batch directory. In order to set up a batch inference job, we will follow these steps:

1. Import the dependencies of your batch job; among the relevant dependencies we include pandas, mlflow, and xgboost:

   import pandas as pd
   import mlflow
   import xgboost as xgb
   import mlflow.xgboost
   import mlflow.pyfunc

2. We will next load start_run by calling mlflow.start_run and load the data from the input.csv scoring input file:

   if __name__ == "__main__":
       with mlflow.start_run(run_name="batch_scoring") as run:
       ...