Managing Model Lifecycle

Managing a model’s lifecycle ensures it remains reliable, reproducible, and maintainable over time. Building ML models is never a set it an forget it procedure. All models deteriorate over time and become less effective at making predictions for a variety of reasons (i.e., data changes, prediction targets change, etc.). In this recipe, we cover versioning, reproducibility measures, document control, and ensuring consistency across training and serving environments.

Getting ready

You’ll prepare a model-saving script and tools to snapshot metadata and validation outputs.

1. Load libraries:

   import numpy as np
   from sklearn.ensemble import RandomForestClassifier
   from sklearn.datasets import make_classification
   from sklearn.datasets import load_iris
   from sklearn.metrics import accuracy_score
   from joblib import dump
   import json

2. Train a model and simulate versioning environment:

   X, y = make_classification(n_samples=800, n_features=20, random_state=2024)
   clf...