Context & Problem
Machine learning models in operation can degrade as data distributions change over time, while many workflows still rely on manual or weakly structured retraining and deployment decisions. This creates limited traceability between data versions, training runs, evaluation results, and promotion decisions, and makes controlled lifecycle management difficult for small teams without enterprise-scale infrastructure.
Solution & Approach
This project proposes and is implementing a lightweight, CI-based MLOps pipeline for structured lifecycle management of machine learning models. The framework is designed to combine controlled data handling, reproducible training, systematic evaluation, drift analysis, rule-based promotion, deployment, and monitoring in a modular workflow where data drift is used as an explicit decision-support signal rather than as a purely passive monitoring metric.
Key Highlights
- Modular MLOps-inspired architecture for controlled training, evaluation, promotion, deployment, and monitoring of ML models
- Data drift incorporated as an explicit input to evaluation and promotion logic, with human oversight preserved in lifecycle decisions
- Reproducible pipeline design linking dataset references, preprocessing, training parameters, evaluation results, and model artifacts
- Explicit separation between candidate and production models through versioning, metadata tracking, and centralized registry logic
- CI-based orchestration planned for preprocessing, training, evaluation, drift analysis, and promotion checkpoints
- Task-aware design intended to support both classification and regression workflows through different evaluation and drift analysis components
- Containerized model serving via FastAPI and Docker as part of a lightweight deployment approach for small-team environments
- Controlled experiments with simulated drift are planned to evaluate decision behavior under changing data conditions
- Project scope is intentionally limited to a lightweight reference implementation, not a production-grade enterprise platform
- Implementation is ongoing, and current development progress can be followed in the linked GitHub repository