MLOps Pipeline
End-to-End ML Model Platform
A production-grade machine learning pipeline spanning from data versioning and model training to API serving and cloud deployment with full CI/CD automation.
The Challenge
Organizations struggle with deploying ML models to production. Data scientists build models in notebooks but lack the infrastructure skills needed to deliver a live, scalable API endpoint. This project aimed to bridge that gap through a complete, production-ready deployment pipeline.
Our Approach
We built an end-to-end pipeline using the Census Income dataset. The solution includes data cleaning and preprocessing using scikit-learn for binary classification, a FastAPI REST API with Pydantic validation and Swagger documentation, and DVC (Data Version Control) for artifact versioning with AWS S3 storage. The API exposes two endpoints: GET for health checks and POST for income predictions.
Testing & Quality
Comprehensive testing covers both model and API layers. The approach includes unit tests, API contract testing, slice-based performance analysis across demographic subgroups, and a documented model card addressing intended use, training data, and ethical considerations. GitHub Actions handles CI/CD with flake8 linting and pytest testing.
Results & Impact
The architecture delivers a fully automated path from code change to live API. The system achieves sub-second prediction latency, automatic validation on every push, and complete model traceability through DVC versioning. Deployment to Heroku is fully automated with S3 model artifact retrieval.