Production-Ready GAN for MNIST Synthesis
Generative Adversarial Network with HuggingFace deployment for handwritten digit synthesis.
PythonPyTorchGradioHuggingFace SpacesDocker
View on GitHub ↗Problem Statement
Building a GAN is one thing — deploying it with production-grade reliability is another. The challenge: design, train, and deploy a GAN with a live interactive demo, showcasing full-stack ML engineering from architecture to CI/CD.
Technical Approach
- Generator with 1.49M+ parameters and Discriminator with 1.46M+ parameters, trained on MNIST
- Training techniques including LeakyReLU activation and Batch Normalization for generator stability
- Production-ready Gradio web app with comprehensive error handling, structured logging, full type hints, and input validation
- Intelligent device detection with automatic GPU acceleration
- Deployed to HuggingFace Spaces with seamless CI/CD integration
Results
- Live interactive demo accessible to end users on HuggingFace Spaces
- Optimized model serialization and containerized dependencies for multi-cloud deployment
- Full-stack ML expertise demonstrated: deep learning, PyTorch, and software engineering best practices (PEP 8, SRP, DRY)