Hold data back
Keep evaluation observations separate from training. For time-dependent telemetry, the evaluation plan should consider timing and avoid leaking later information into earlier predictions.
RAIN / Resources
A new modelhas to earn its place.
How candidate evaluation, held-out data and retained model history support a more inspectable local learning loop.
Model evaluation explainer
INTELLIGENCE BELONGS WHERE THE DATA LIVES.
01 / THE IDEA
Learning and promotion are different decisions.
Retraining produces a candidate. It does not automatically make that candidate the active model. In RAIN’s simulator, the candidate must beat the incumbent on held-out data before promotion. Rejected evaluations are recorded too, so the reason for keeping the current model remains visible.
Make the comparison meaningful
Compare candidate and incumbent under the same evaluation conditions. Choose criteria tied to the operational question rather than relying on a single attractive score.
Keep the previous state recoverable
The simulator includes promotion and rollback components. A field pilot still needs to verify recovery behavior, compatibility and the practical process for reviewing a change.
02 / IN PRACTICE
Put the thinking into practice.
Define the scope, preserve the boundary, and make the outcome something a person can inspect.
- 01
Establish the incumbent
Identify the active model and the baseline evaluation. Define the operating conditions and what improvement means before reviewing a candidate.
- 02
Evaluate the candidate
Apply the agreed comparison to held-out data. Inspect failures and coverage across relevant conditions, including periods that differ from the training set.
- 03
Record the outcome
Promote only after the gate is met; otherwise retain the incumbent. Preserve the version, evaluation and decision for later review.
03 / A FEW DETAILS
Worth understanding.
Does a better evaluation score prove field value?
No. It supports a specific comparison on the evaluation data. Real signal quality, changing conditions, alert usefulness and operating stability still need field validation.
What happens when a candidate is worse?
The incumbent stays active. The rejected candidate’s evaluation is still recorded in the history rather than disappearing from the account of model changes.
BUILD WITH RAIN
One site. One question.
A useful next step.
RAIN is preparing for its first field pilot. Start by defining the environment, the data boundary, and what success should look like.