A Q&A From Inference To Training: The Inside Story Of Baseten’s Newest Product

This post introduces Baseten's new training infrastructure, a significant expansion of its AI model deployment and serving platform. Key technical contributions include: a flexible training environment that accepts custom code, images, and repositories without heavy abstractions; persistent storage primitives to accelerate iteration by avoiding repeated data downloads; a pipeline for seamless transition of training checkpoints to inference for end-to-end deployment and evaluation; support for diverse model types beyond text-based LLMs, including audio models; and multi-node training capabilities for complex tasks like longer sequence lengths. The platform is designed for developers who need to debug, tweak parameters, and experiment with non-standard approaches. Migration is simplified by Baseten's unopinionated approach, allowing users to bring existing training pipelines with minimal code modification. The integration with Baseten's inference expertise is highlighted, with a focus on understanding inference requirements (time to first token, throughput) early in the training process. Customer feedback, particularly regarding private Docker image support, drove rapid feature development, demonstrating an FDE-driven development cycle.