Training Data

Data used to train AI models to perform specific tasks

Overview

Training data is the information we use to teach AI models how to do their job. Just like humans learn from examples, AI models learn from carefully selected and prepared data.

What Makes Good Training Data?

Quality training data should be:

  • Accurate and reliable
  • Representative of real-world cases
  • Well-balanced across different types
  • Properly labeled when needed
  • Free from harmful biases
  • Clean and consistent

Types of Training Data

  • Labeled Data
    • Has correct answers provided
    • Used for supervised learning
    • Examples: tagged images, categorized text
  • Unlabeled Data
    • No answers provided
    • Used for unsupervised learning
    • Examples: raw text, untagged images
  • Synthetic Data
    • Artificially created
    • Helps fill gaps in real data
    • Protects privacy

Best Practices

  • Regular quality checks
  • Careful documentation
  • Version control
  • Bias monitoring
  • Privacy protection
  • Regular updates

Common Challenges

  • Getting enough data
  • Ensuring quality
  • Maintaining privacy
  • Handling bias
  • Managing costs
  • Keeping data current