Real‑Time Sports & Event Analytics Platforms bring together streaming telemetry, computer vision, audio processing and generative AI to deliver live insights for broadcasters, teams, venues, sponsors and betting operators. This topic examines how systems integrators (e.g., Infosys) and major cloud AI providers (AWS, Google Cloud) combine infrastructure, model services and integration tooling to meet stringent latency, reliability and compliance requirements. Advances in multimodal models and managed ML services have made it practical to extract play‑level intelligence, automated highlights, contextualized commentary and personalized fan experiences in near real time. Key components include high‑throughput data pipelines, low‑latency model inference at edge or cloud, model fine‑tuning for domain accuracy, and visualization/automation layers. Representative tools: Google Gemini (multimodal generative models via Google AI and Vertex AI) for contextual analysis; Domo for data integration, visualization and operational dashboards; IBM watsonx Assistant for enterprise virtual agents and orchestrated automation; Together AI for fast inference and scalable GPU training of open‑source models; and conversational assistants like Anthropic’s Claude for scripted analysis and narration. Infosys typically plays the role of systems integrator and managed‑services partner, combining cloud offerings, domain connectors and operational support to deploy end‑to‑end workflows. AWS and Google Cloud provide the core building blocks — streaming services, model hosting, edge deployment and compliance controls — that teams evaluate based on latency, model capability, operational maturity and cost. Current 2026 trends emphasize hybrid/multi‑cloud deployments, deployment‑side model tuning, provenance and privacy controls, and tighter integration with broadcast and betting ecosystems. Choosing an AI partner centers on matching latency SLAs, multimodal model needs, integration breadth (connectors and visualizations) and operational support for continuous model retraining and observability.