Machine Learning & MLOps ยท 2025
DIABETES MLOPS
A production-grade MLOps pipeline implementing the complete ML lifecycle from data versioning to model training and monitoring.
The Problem & User Needs
Developing an ML model is only a small fraction of deploying AI in production. The challenge was to construct a robust MLOps ecosystem that ensures reproducibility, automates retraining, tracks experiments, and provides a scalable inference API.
Role & Contributions
MLOps Engineer
Designing the pipeline architecture, implementing DVC/MLflow tracking, building the FastAPI service, and configuring Docker deployments.
Research Methods
Researched industry standards for ML deployment architectures. Evaluated various orchestration tools (Airflow vs Prefect) and selected Prefect for its Pythonic simplicity. Analyzed model drift monitoring requirements.
Design Process & Architecture
Mapped out the data flow from raw extraction through DVC to model training logged via MLflow.
Designed a FastAPI REST interface supporting both single predictions and batch CSV processing.
Formulated automated testing strategies using pytest for both data validation and API endpoints.
Measurable Outcomes
Successfully established complete data lineage and experiment reproducibility. The API handles scalable concurrent requests, and the CI/CD pipeline automates testing and containerization, drastically reducing model deployment time.