You need to record all preprocessing steps before sending data for prediction or training. How would you incorporate this into using MMCB?

Capture each preprocessing step as a serializable artifact (e.g., a JSON description or a Python pickle). Store these artifacts in MMCB’s metadata store alongside the model. When a new dataset is processed, log the exact sequence of steps and any parameters used. This allows reproducibility: you can replay the same pipeline for future predictions or retraining. Additionally, expose the metadata through MMCB’s API so downstream consumers can programmatically retrieve the preprocessing steps and verify compatibility.