Learning path
Good AI starts before training begins.
This path explains the quiet work behind useful AI: choosing data, cleaning records, labeling examples, separating test data, and checking whether the model performs fairly across different cases.
Recommended order
Follow the data path
Training basics
Understand how examples help a model learn patterns and make predictions.
02Data cleaning
See why duplicates, missing values, wrong formats, and noise weaken results.
03Trusted data
Judge source quality, permission, freshness, relevance, and documentation.
04Labels
Learn how labels become teaching signals and why consistency matters.
Data rule
A model can only learn from the examples it receives.
Training data is not just raw material. It shapes what a model notices, ignores, repeats, and gets wrong. Poor data can make a model look impressive in a demo while failing on real users.
A useful data workflow asks where the examples came from, whether permission is clear, what the data represents, what is missing, and how performance will be tested on examples the model has not seen before.
Data quality warning signs
- The source is unclear or permission is undocumented.
- Important groups, cases, or languages are missing.
- Labels are inconsistent between reviewers.
- Testing uses examples that are too similar to training data.
Next guides
Finish with testing and bias review
Training, Validation, and Test Data
Learn why separate datasets help reveal whether a model handles new examples.
BiasTraining Data Bias Explained
See how missing or unfair examples can shape model behavior and hide weak spots.
ChecklistTraining Data Checklist
Use a practical checklist to score source quality, labels, coverage, freshness, and bias.