Setting Up Deployment Pipelines

Deploying models reliably requires automation. Like mentioned previously, since model performance can (and will) change over time, it’s necessary to implement systems that can handle this without constant human intervention. In this recipe, we’ll show how to integrate model serialization, validation checks, and serving logic into a CI/CD pipeline using scikit-learn constructs, enabling consistent transitions from development to production.

Getting ready

You’ll need tools to train a pipeline, export it, and programmatically validate performance.

1. Load libraries:

   import numpy as np
   from sklearn.datasets import make_classification
   from sklearn.preprocessing import StandardScaler
   from sklearn.linear_model import LogisticRegression
   from sklearn.pipeline import Pipeline
   from joblib import dump, load
   from sklearn.metrics import accuracy_score

2. Create data and pipeline:

   X, y = make_classification(n_samples=500, n_features=10, random_state=2024...