Home▸AI & Machine Learning▸Prioritising Machine Learning Projects: RICE Scoring and the Value-Effort Matrix

📊 RICE Score Lab

Score candidate machine learning projects with the RICE framework and see them plotted live on a 3D value-effort matrix and roadmap.

AI & Machine Learning3DAdvanced60 FPS

A 3D value-effort matrix plots candidate machine learning projects by effort and expected value, with bar height showing each project's RICE score — and a live roadmap budget shows which ones a team can actually fund this year.

🔬 What It Demonstrates

RICE = (Reach × Impact × Confidence) ÷ Effort turns fuzzy pitches into one comparable number. Bar position shows the value-effort trade-off; bar height and color show the score and whether the roadmap budget can afford it.

🎮 How to Use

Shape "Your Project" with the Reach, Impact, Confidence and Effort sliders and watch its rank change against six real-world ML candidates. Drag the team capacity slider to see which projects survive the cut for a 12-month roadmap.

💡 Did You Know?

RICE deliberately keeps Effort in the denominator so that a merely "good" project with tiny effort can outrank a "great" project that would eat a whole team's year — cheap wins compound.

⚙ Under the hood

Score candidate machine learning projects with the RICE framework and see them plotted live on a 3D value-effort matrix and roadmap.

machine learningrice scoringvalue effort matrixproject prioritizationalgorithmsdata scienceautomationThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install