Building a Production MLOps Pipeline on AWS SageMaker for Telecom Churn

It looks like you've shared a detailed breakdown of an end-to-end machine learning pipeline for predicting customer churn in the telecom industry, including various Python scripts and tests. Let's go through each part to ensure everything is clear and complete:

1. Data Preprocessing (preprocess.py)

This script handles data cleaning and feature engineering before model training.

• Key Functions:

  • load_data: Loads raw data from S3 or another source.
  • clean_data: Removes duplicates, fills missing values, etc.
  • encode_features: Encodes categorical features (e.g., one-hot encoding).
  • split_data: Splits the dataset into training and validation sets.

• Tests:

  • Ensure that data is loaded correctly.
  • Verify that cleaning functions remove duplicates and handle missing values properly.
  • Confirm that feature encoding works as expected.
  • Check if splitting results in correct proportions (e.g., 80% train, 20% test).

2. Model Training (train_model.py)

This script trains the machine learning model using the preprocessed data.

• Key Functions:
  • train: Trains a logistic

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