How to Prepare Large-Scale Training Data for Large Model Training

This article discusses the key steps in preparing large-scale training data for training large AI models, including defining the problem and data requirements, data collection from various sources, and data cleaning and preprocessing.

Details

The article outlines a comprehensive approach to preparing large-scale training data for training complex AI models. It starts by emphasizing the importance of clearly defining the problem and understanding the specific data requirements, such as the type of data (text, images, audio), quality, diversity, and scale needed for effective model training. The next step is data collection, which can involve leveraging public datasets, web scraping, utilizing APIs, crowdsourcing, and generating synthetic data through simulations. Finally, the article discusses the critical data cleaning and preprocessing stage, where the raw data is cleaned to remove noise and irrelevant information, handle missing values, and standardize the data format. These steps are crucial to ensure the quality and usability of the training data for large model training.