Generative AI Data Infrastructure: How to Train Large Language Models (LLMs) with Deep Lake

Large language models (LLMs) are taking the world by storm, with companies scrambling to implement them into their products. These AI systems utilize deep learning algorithms to generate and interpret human language and can be trained on massive amounts of text data. However, their size and computational requirements make them challenging to deploy, and there are concerns about the ethical implications of using these models. To address common issues with LLM training, companies must build a scalable data flywheel to efficiently acquire, retrain, and evaluate data to improve LLM performance. This includes addressing data storage and retrieval bottlenecks, ensuring data quality, handling multimodality, and managing deployment and maintenance costs.