Evaluate when on-device AI reduces latency, cost, or privacy exposure and how to balance engineering and operational tradeoffs for production use.
Interactive checklist and evaluation worksheet to determine whether and how to deploy AI models on edge and mobile devices. Covers latency, throughput, device constraints, model size, compression strategies, privacy and data minimization, offline behavior, update and rollback plans, monitoring, and a simple feasibility score to guide go/no-go decisions.
Interactive checklist to help teams assess whether on-device or edge AI deployment is likely to deliver meaningful benefits (latency, privacy, cost, reliability) while revealing key engineering tradeoffs for updates, monitoring, security, and maintainability.
Short, actionable research briefs that describe promising AI opportunities, propose bounded experiments, identify required data and tooling, surface likely risks, and suggest measurements to decide whether to scale.