This description is based on a conversation with 'Sam', which took place over several days in November 2024
Pre-processing is the critical first step in preparing raw data for AI model training, ensuring accuracy and efficiency. It begins with data collection and ingestion, where vast amounts of structured and unstructured data—often terabytes to petabytes—are gathered from various sources. Next, data cleaning, filtering, and normalization refine this massive corpus by removing duplicates, handling missing values, and standardizing formats. This process can reduce the dataset by 30-70%, eliminating noise and ensuring consistency for training.
Following this, feature extraction and engineering refine the dataset by selecting relevant attributes and transforming them for better model performance. For instance, Meta's Llama 3 was pre-trained on approximately 15 trillion tokens of text gathered from publicly available sources, with models ranging from 8 billion to 70 billion parameters. Similarly, OpenAI's GPT-4, while the exact number of parameters hasn't been publicly disclosed, has been rumored to have around 1.76 trillion parameters. Finally, data labeling and augmentation enhances the dataset with annotations and synthetic variations, optimizing it for machine learning applications. Effective pre-processing significantly improves AI model accuracy, reduces bias, and cuts computational costs by reducing unnecessary data load.