Machine learning · Jun–Jul 2022
Modelsmith
A Django web application bringing multiple machine learning architectures into one model-evaluation interface.
Overview
Bringing machine learning into a usable application
Modelsmith brings different machine-learning architectures into a shared web interface. Instead of moving between separate notebooks, users can explore image classification, image generation, prediction, and language-processing demonstrations in one application.
I developed it with Ashwani Ahlawat. The work combined model implementations with a Django application and technical explanations of the ideas behind them.
Technical detail
One application, several model families
Separate Django apps organise CNN classification, GAN generation, neural-network prediction, and NLP workflows. The project covers more than ten architectures, including custom ResNets and generative adversarial networks.
The web layer connects inputs and results to the model implementations. This adds the application structure that a standalone training notebook does not provide.
Beyond the model
Docker, Compose, Caddy, and Gunicorn configuration support the application’s deployment structure. The accompanying technical material explains linear algebra, backpropagation, and model configuration, connecting the user-facing demonstrations to their mathematical foundations.