ML Production Pipeline
FastAPI + Docker pipeline with monitoring
1Problem
The client had ML models running in Jupyter notebooks with manual execution. No version control, no monitoring, and predictions required data scientists to run scripts manually. Model drift went undetected for months.
2Solution
Built an end-to-end MLOps pipeline: FastAPI serving layer with Docker containerization, Prometheus metrics for latency and prediction distribution monitoring, automated retraining triggers based on data drift detection, and CI/CD with GitHub Actions for model deployment.
3Technical Details
Complete machine learning pipeline built with FastAPI and Docker, featuring production-grade monitoring and real-time predictions for purchase transaction analysis. Includes comprehensive logging, metrics collection, and automated retraining capabilities.
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