Leveraging GraphQL for Continual Learning in Real-Time ML Systems - Aryan Magoon
Many large-scale ML systems are still stuck on the batch-training paradigm: models are trained and deployed on static data and updated only periodically. In a world of ever-changing data, this approach can limit a model’s ability to adapt and perform under new conditions. By leveraging GraphQL and its real-time data strengths, we can boost performance in production, facilitate continual learning, and create intelligent networks that can adapt and learn without delay.