This topic covers real‑time video tracking and markerless biomechanics solutions that extract pose, kinematics and contextual behavior from live video and sensor streams for sports analytics, performance monitoring and clinical assessment. Recent advances shift computation toward edge AI vision platforms to meet strict latency, privacy and connectivity requirements while cloud MLOps and foundation models enable rapid iteration, personalization and large‑scale analytics. Key platform patterns include on‑device inference and low‑latency pipelines (edge vision SDKs and optimized pose models), multimodal sensor fusion and temporal reasoning (combining video with IMUs or event cameras), and managed ML stacks for training, deployment and monitoring. Representative tools from the provided descriptions: Archetype AI’s Newton — a “Large Behavior Model” designed for real‑time multimodal sensor fusion and reasoning deployable on edge or on‑premises; Google Gemini — multimodal generative models and developer APIs useful for augmenting pose outputs, labeling, and natural‑language reasoning over tracked events; Vertex AI — Google Cloud’s end‑to‑end platform for model training, deployment and monitoring in production; and Mistral AI — provider of open, efficiency‑focused models and an enterprise production platform emphasizing privacy and governance. As of 2026, priorities for adopters include low latency on‑field inference, secure on‑premises or hybrid deployments, model efficiency for battery‑constrained devices, and tooling for domain adaptation and athlete‑specific calibration. Evaluations should weigh latency, accuracy of 2D→3D reconstruction, ease of multimodal integration, privacy/compliance options, and production features (MLOps, monitoring, model governance) when selecting solutions for sports analytics and markerless biomechanics.