This project delivers a fully end-to-end ML system for email spam detection, taking a model from exploration to production with reliable monitoring and retraining. It starts with data profiling and lightweight feature engineering (text length, word counts), then benchmarks multiple classifiers and vectorizers (BoW, TF-IDF across Naive Bayes, Logistic Regression, Random Forest, XGBoost) using cross-validation, with MLflow tracking runs, metrics, artifacts, and models. Precision is prioritized to minimize false positives, and the selected pipeline (CountVectorizer + Naive Bayes) is serialized for serving. The production setup includes a FastAPI backend for inference and a simple Streamlit frontend for user input, both containerized. Docker Compose orchestrates services and mounts shared storage for models and data. Operational robustness is added via data drift detection with Evidently AI, comparing current inputs to reference distributions (labels, predicted probabilities, text descriptors). When drift is detected, an Airflow DAG conditionally triggers automated retraining inside a Dockerized environment, writes the updated pipeline back to the serving path, and keeps lineage clear. The result is a practical blueprint for small teams: reproducible experiments, metrics-driven model selection, one-command local deployment, and a scheduled governance loop that catches drift and refreshes the model without manual check which is ready to be lifted to cloud infrastructure when needed.