机器学习生命周期. Copy the source, paste into the official playground, done. No coordinates — every position is stated relative to something else.
node data "Data collection"
node prep "Prep & features" below data
node train "Training" below prep
node eval "Evaluation" below train
node deploy2 "Deployment" below eval
node mon "Monitoring" right of deploy2
edge data -> prep
edge prep -> train
edge train -> eval
edge eval -> deploy2 "promote"
edge deploy2 -> mon
edge mon -> data "drift retrain"npx reladraw ml-lifecycle.reladraw -o out.svgData to features to training to evaluation to deployment, with monitoring feeding drift back into collection.. Use it in MLOps onboarding — the loop edge is the whole point.
node new_id 'New label' below anchor — placement is relative, so the solver re-flows the whole diagram instantly.edge a -> b 'your label' — labels are the fastest way to carry rates, protocols or percentages.below to right of rotates a branch sideways without touching any other line.Declare each box with node, connect them with edge, and state one position per node relative to another. Paste the source from this page into the official playground and it renders immediately.
Yes — the source is free to copy, modify and ship in docs, READMEs, books or client decks. No attribution required.
Render in the playground and export SVG, or run it locally: npx reladraw file.reladraw -o out.svg for CI-friendly builds.