End-to-end infrastructure for training and inferencing open weight models (docs.appliedcompute.com)

A new end-to-end infrastructure for training and inferencing open weight models has been announced, streamlining the process for developers in the AI/ML community. The solution, referred to as AC2, allows users to quickly set up their environment by leveraging coding agents like Claude Code or Codex. This framework includes a comprehensive SDK (ac2.sdk) for managing datasets and workloads, runtime tools (ac2.runtime) for defining complex agent interactions, and an observability component (ac2.tracing) powered by OpenTelemetry for tracing and analysis. The significance of AC2 lies in its simplified approach to model training and deployment. With options for various training methodologies—such as supervised fine-tuning and on-policy self-distillation—it caters to diverse project needs. The infrastructure enables developers to focus on evaluation and deployment without getting bogged down by the intricacies of setup and management. By routing inference through customizable policies and endpoints, AC2 also ensures efficient traffic management, making it a pivotal resource for improving productivity and enhancing model performance in AI applications.