ML Pipeline & Model Serving
End-to-end pipeline for training, evaluation, and deployment of ML models with experiment tracking.
A production-ready ML pipeline that handles training, evaluation, versioning, and deployment. Built for reproducibility and scale with experiment tracking and model registry.
Problem
Training and deploying models across environments was manual and error-prone. We needed a single pipeline that could version data, track experiments, and serve models consistently.
Solution
Designed an end-to-end flow using PyTorch for training, MLflow for experiment tracking and model registry, and containerized serving with Docker. The pipeline supports automated retraining and A/B deployment.
Outcome
Reduced time from experiment to production and improved reproducibility. All runs are logged with metrics, artifacts, and parameters for easy comparison and rollback.
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