Powering Inference for the Continual Learning Era

Continual learning represents a transformative shift in machine learning, where models improve incrementally with each user interaction, contrasting with the traditional static approach that relies on periodic updates. This paradigm is being advanced by Trajectory, which focuses on integrating production traces to identify model failures and retrain models continuously. Core challenges include developing an infrastructure that supports dynamic models, allowing them to evolve with user input, and creating a product layer that unifies model learning with product functionality. Baseten and Trajectory have collaborated to develop an inference layer that rapidly deploys updated models, enabling swift iteration and improvement, with the ultimate goal of serving numerous model variants across different users. The process involves merging LoRA adapters into base models, validating these before deployment, and using A/B testing to assess performance. Despite significant progress, the journey towards achieving real-time model improvement and serving diverse models to individual users is ongoing, with future enhancements focusing on further optimizing the deployment and training processes.