✨ Proven on Kaggle: +0.4% AUC improvement on Titanic dataset →

NanoML: governed experiment memory layer for ML systems

In the future, ML infrastructure won't be dashboards for humans. It will be machine-readable memory systems for agents.

NanoML Dashboard - Data Commits and Model Runs Visualization

The Cost of Dirty Data

⚠️

Hidden Quality Issues

19% missing values, 10% duplicates go undetected until production failures

📉

Model Underperformance

Training on dirty data degrades AUC, precision, and recall metrics

⏱️

Weeks of Manual Debugging

No systematic way to measure data quality impact on models

Catch Issues
Before GPU Burn

  • ✓ Profile 25M+ rows in <10 seconds—instant feedback, not hours of training
  • ✓ Detect train-test skew, missing values, and class imbalance before wasting GPU cycles
  • ✓ Save 80%+ debugging time—find problems in data, not failed model runs

Promising Results From
Optimized Datasets

  • ✓ Improve model metrics (NE, AUC, CTR) without changing architecture
  • ✓ Generate actionable fixes—sample weights, filter masks, and imputation strategies
  • ✓ Turn raw data into vintage quality with automatic manifest application

Full Training Data
Traceability

  • ✓ Track every data transformation from raw to model-ready
  • ✓ Reproduce any training run—know exactly which samples were used
  • ✓ Debug model failures by tracing back to data quality issues with complete audit trail

Works With Your Existing Stack

NanoML integrates seamlessly with your data science workflow. No migration, no vendor lock-in.

Validation Pipeline Built for ML Teams

Four-step workflow from detection to deployment

1

Detect

Schema validation, missing values, duplicates, outliers, label noise

2

Fix

Remove duplicates, impute missing values, filter anomalies automatically

3

Measure

Compare before/after model metrics: AUC, precision, recall, F1

4

Ship

Deploy models with proven performance gains and documented ROI