An in-depth exploration of the rise of human-centered, interactive machine learning. This article examines how Streamlit enables collaborative AI design by merging UX, visualization, and automation. Includes theory, architecture, and design insights from the ML Playground project.

A Streamlit-powered machine learning playground that automatically detects classification or regression tasks, builds pipelines with preprocessing, trains models interactively, and visualizes metrics using Plotly. Backward-compatible, fully responsive, and deployable on Streamlit Cloud or Docker.

A tool to support interactive machine learning for cryoET data

Napari plugin for CellCanvas interactive segmentation

An interactive Streamlit web application that uses a Random Forest classifier to predict penguin species based on real-time user inputs.

Interactive GPT-2 inference explorer with token probability visualization, entropy curves, confidence heatmap, and sampling strategy comparison. Built on nanoGPT.

Interactive machine learning for cryogenic electron tomography (cryoET) data