MLV Viewer

The MLV Viewer enables researchers and engineers to visualize multi-layer ML outputs without training or data collection. It accepts structured CSV or JSON inputs describing metrics, coordinates, and relations, and supports several visualization types such as scatter, line, heatmap, matrix, and parallel coordinates. The tool targets ML researchers, data scientists, and engineers seeking quick, reproducible visuals across experiments.

Conceptually, the tool applies normalization and scaling, maps inputs to axes, assigns perceptually uniform color mappings, and stacks visualization layers to reveal cross-cutting patterns. It enforces a single input schema so users can switch visualization types without reformatting data. Optional features include thresholds, filtering, downsampling, and per-layer grouping to focus on areas of interest.

A core value is consistency and automation: the viewer exports SVGs for reports and a JSON summary with key statistics, enabling integration into CI/CD dashboards and ML pipelines. It helps identify data drift, compare model behavior across runs, and validate feature impacts. The tool does not train models and assumes cleaned input data.

Differentiators include multi-type rendering from a single input schema, deterministic renders, exportable summaries, and lightweight reduction options, making it suitable for scalable ML visualization in research and production workflows.