← Back to Glossary
Training Data Preparation
Definition
Training data preparation is the process of collecting, cleaning, transforming, and organizing raw data into a format suitable for training machine learning models, including quality assessment, normalization, and validation.
Purpose
The purpose of training data preparation is to ensure that machine learning models are trained on high-quality, representative, and properly formatted data that will lead to reliable and accurate model performance.
Key Characteristics
- Data cleaning to remove errors, duplicates, and inconsistencies
- Format normalization and standardization across data sources
- Quality assessment and validation against defined criteria
- Handling of missing values and outliers
- Documentation of data provenance and transformations
Usage in Practice
In practice, training data preparation is used before any machine learning project to transform raw enterprise data into training-ready datasets, ensuring that models learn from accurate and representative examples.
One implementation of this concept is offered by Kenaz through the AI Data Preparation service.