Implementing a custom MLflow Python model

Let's first describe the steps to implement a custom MLflow Python model without any extra preprocessing and postprocessing logic:

1. First, make sure we have a trained DL model that's ready to be used for inference purposes. For the sake of learning in this chapter, we include the training pipeline MLproject in this chapter, so that we can easily produce a fine-tuned DL model. To run the training pipeline, make sure you have the virtual environment set up for this chapter by following the README file in this chapter's GitHub repository and set up the environment variables accordingly (https://github.com/PacktPublishing/Practical-Deep-Learning-at-Scale-with-MLFlow/blob/main/chapter07/README.md). Then, in the command line, run the following command to generate a fine-tuned model in the local MLflow tracking server:

   mlflow run . --experiment-name dl_model_chapter07 -P pipeline_steps=download_data,fine_tuning_model

Once...