Free Tool
Is Your Dataset Ready for Machine Learning?
Most models don't fail in training — they fail because the data was never going to work. Severe class imbalance, a training set that doesn't look like production, leakage that fakes a great accuracy score. Upload your dataset and find out where it stands before you burn a single GPU hour.
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What "ML-ready" actually means
- Enough rows to train without overfitting
- Class balance — no single label drowning out the rest
- Distribution that matches what the model will see in production
- Completeness of the features that actually matter
- Duplicates and leakage that inflate accuracy and lie to you
- Field statistics that reveal hidden encoding and scale problems
New to this? Start with What Is AI Readiness? for the full framework, or work through the free readiness checklist before your next model.