Define what success means
Choose a useful objective and the errors the business cannot afford to overlook.
Choose a model for the result that matters.
Models. Compare predictive model candidates against a business objective. Inspect performance on important cases before deciding whether a candidate needs more work.
Product walkthrough. Press Space or Enter to pause or play.
Models. The scores and errors behind the choice.
Compare the candidates
Review overall scores alongside results on important groups of cases.
Investigate regressions
Inspect a candidate that improves the headline score but misses more high-risk cases.
Record the next decision
Keep the comparison and rationale with the decision to continue the study or consider a later release.
A better score needs a better case.
Compare three candidates, inspect the slice that fails, and retain the reason the team chooses to continue the study.
Review three candidates against the same 2,000 shipments. A higher overall score is only part of the decision.
Keep Trial 1 as the reference. Compare the overall score with recall on hazardous loads, where missed cases matter most.
Trial 3 leads overall but misses the hazardous-load guardrail. Record Continue study and improve recall before proposing a release.
Late delivery risk
Compare the overall scores.
Overall quality score (PR-AUC) · higher is better
Trial 12,000 samples
Trial 22,000 samples
Trial 32,000 samples
Trial 1 is the baseline. Every candidate uses the same evaluation sample.
Understand the inputs
behind the comparison.
Know what your model learned from.
Keep the objective, datasets, and feature releases connected to each training run. Return to the inputs behind a result.
Connected to the candidate
Input relationships01Modeling objective
02Dataset release
03Feature release
04Training run
Keep the decision connected to the evidence.
Keep the comparison beside the candidate and its inputs. Plan release and monitoring separately once the team has a model worth advancing.
From evaluation to operation
Review contextA model you can follow.
- Evaluation results
- Version and release history
- Delivery and monitoring requirements
In practice
Meridian compares three candidates for predicting which shipments will miss the 21:00 cutoff. Trial 3 improves the overall score, but hazardous-load recall falls below the 90% minimum. Maya records Continue study with a reason to improve recall before proposing a release.
Explore the solutionDiscuss the steps after comparison.
The walkthrough ends with Continue study. Contact us to scope release, serving, and monitoring for your deployment. The following illustrates the questions to resolve.
Review a fixed sample, the slices that matter, and your guardrails before moving a model version forward.
Discuss the configuration needed for batch predictions or an online deployment. Identify the reviewed model version and release requirements.
Plan which observations and labels to collect, how to monitor results, and who should review a proposed update or rollback.
The model lifecycle
Build confidence before release.
- 01
Fixed evaluation sample
- 02
Quality, slices & guardrails
- 03
Model version review
Keep insufficient data visible in the evaluation.
Discuss a model comparison.
Bring a predictive candidate and the outcome you care about. We’ll discuss evaluation, important failure cases, and any later deployment requirements.