🛰 ML Pipeline — end-to-end ML lifecycle
Cleaning the data does the heavy lifting, the test set stays sealed, and deployment loops back — a river, not a line.
at rest
← Suite Home
▶ Play
journey
speed
6
⏭ Skip preprocessing
↺ Reset
stage 1 / 8 · Data Eng
data in motion
Quality
--
waiting for data
Drift
0.00
⏱ Time ribbon — segment width = real-world time share
Preprocessing eats most of the clock · training is a sliver
Messy & scattered
Data is collected from mismatched
sources
— and some never reconciles.
Heavy lifting
Preprocessing
moves quality far more than the model does — try "Skip".
Sealed test set
The
test
stream is locked in a vault the model can't see while training.
A living loop
Deployed models
drift
— monitoring loops back to retrain.