Streamline model development with foundation model tuning that enables teams to customize state-of-the-art detectors and segmentation networks to their data in just a few clicks. Training workflows scale effortlessly across multi-GPU and multi-node environments, reducing epoch times and supporting rapid experimentation. When models are ready for production, they can be deployed with minimal effort and integrated seamlessly via REST APIs, eliminating the need for Kubernetes or specialized DevOps expertise. Intelligent inference operations maintain reliable performance at scale, automatically adjusting compute resources to match traffic demands while providing real-time visibility into latency, throughput, and system health.