Most teams ship a model and move on. Nobody runs a systematic audit. We analyze your predictions against ground truth — classification, regression, or text/NLP — and return a complete health report in under 24 hours: health score, drift, failure modes, and a ranked action plan.
Data drifts. Pipelines shift. Edge cases accumulate. Most teams don't find out their model is broken until a business metric tanks — never before.
"We discovered our fraud model was missing 30% of cases. It had been drifting for two months. Nobody had an alert."— ML Engineering Lead, Series B fintech
Every audit covers the dimensions that matter in production — not just accuracy. Interactive charts included in every report.
A single 0–100 health score with Good / Needs Attention / Critical label. Instant overview of your model's production status, top 3 risk signals, and the single highest-impact action your team should take this week — written in plain language for engineering leads and product managers alike.
Detects missing values, severe class imbalance, duplicate rows, low-variance columns and insufficient sample sizes before they corrupt your metrics. Includes a class distribution chart and an imbalance ratio flag — because garbage data in means a garbage diagnosis out.
Far beyond a single accuracy number: Precision, Recall, F1-score, AUC-ROC, Average Precision (PR-AUC), Confusion Matrix, and per-class breakdown. Interactive ROC curve chart visualizes the full operating-point tradeoff so you know where to set your decision threshold.
Kolmogorov-Smirnov test on prediction distributions over time. Detects positive-rate drift, score compression, and distribution shape anomalies. Interactive drift timeline chart shows early-period vs late-period prediction rates — the early warning system your MLOps team needs.
Automated disparate impact analysis across all categorical segments in your dataset. Measures F1-score variance by demographic or business group, flags subgroups with performance gaps > 10 pp, and highlights EU AI Act / EEOC regulatory risk. Includes an interactive subgroup bar chart.
Is your model's confidence score actually trustworthy? We compute Brier Score, Expected Calibration Error (ECE), and a full Reliability Diagram (calibration curve). Detects overconfident models, probability compression, and systematic under/over-estimation — critical for risk scoring and fraud models.
Goes beyond aggregate metrics to map where and why your model fails. Surfaces feature-error correlations, false positive / false negative breakdown by segment, high-error density zones, and systematic misclassification patterns. Turns opaque errors into debuggable root causes.
Scans every input column for near-duplicate features, zero-variance columns, extreme skew (> 10×), high cardinality, and feature leakage signals. Identifies redundant features that inflate training cost without adding predictive power — and flags columns that may cause silent failures in production.
Evaluates your model's operational maturity: Is timestamp logging in place for drift monitoring? Are probability scores exposed for downstream systems? Are there sufficient samples per time period? Surfaces MLOps blind spots before they cause production incidents — the checklist your SRE team will thank you for.
Every finding is automatically converted into a P0 / P1 / P2 action item with effort estimate (S / M / L) and a Python remediation code snippet. No vague recommendations — your team gets a sprint-ready backlog. P0 items are blockers; P1 are high-impact; P2 are improvements. Paste it directly into Jira or Linear.
Export a CSV with y_true and y_pred columns. Add y_score for probability analysis, timestamp for drift detection, and any feature columns for deeper analysis.
Our engine runs 8 independent analyses across all sections. No source code needed. No infrastructure access required. Just predictions.
A structured JSON report (or premium PDF for paid tiers) with your health score, every finding, and a prioritized action plan ready to paste into your sprint board.
No retainer. No monthly subscription. Pay once per audit.
Money-back if we don't find 3 new improvements
A CSV file with at minimum two columns: y_true (ground truth) and y_pred (model predictions). No source code, no infrastructure access needed.
We sign an NDA before you send anything. Your data is used solely for the audit and deleted after delivery. Anonymize sensitive identifiers before sending.
Binary classification, multiclass classification, regression, text classification, text regression, and text similarity/ranking. Tabular and NLP. Specialties: fraud detection, churn prediction, risk scoring, sentiment analysis, content moderation.
Yes. The full audit is available via REST API. See the API docs or try it interactively in the demo tool.
If the Full Audit doesn't surface at least 3 actionable improvements you didn't already know about, we refund you in full. No questions asked.
Yes. After the audit, we offer implementation sprints starting at $1,800 for 3 days of hands-on ML work. About 30% of audit clients take this option.
No sign-up, no credit card. Upload your CSV and get a real health score in seconds.
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