Data ingestion is the process of obtaining data from different sources and bringing it into a common repository, pipeline, or processing environment. In AI workflows, ingested data is often cleaned, transformed, validated, and prepared before it is used for training, fine-tuning, evaluation, or retrieval.
Data ingestion is the process of extracting data from various sources and integrating it into a central location for further processing and analysis. In the context of generative AI, data ingestion involves extracting information from different data sources, such as clinical forms, patient records, or unstructured text, to train and fine-tune generative AI models. The ingested data is typically processed and transformed to ensure its quality and consistency before it is used to train the generative AI models. This process may involve data cleaning, feature engineering, and data augmentation techniques to improve the model's performance and generalization capabilities. For more information, see Use generative AI for utilization management.