Insights, ideas, and experiments at the edge of machine intelligence
AI-in-the-loop transforms model development by improving optimization, speed, and production readiness for real-world systems.
Model zoos accelerate experimentation but fall short in production AI. Learn why reusable models break under real-world constraints.
AutoML can build models, but not all models run in production. Learn why production AI requires a systems-first approach.
AI model retargeting helps models adapt to new hardware. Learn how it enables portability and reliable AI deployment.
AI inferencing—not training—often determines whether models succeed in production. Learn how deployment constraints reshape AI system design
Edge AI deployment fails due to latency, power, and hardware constraints. See how constraint-first design enables production-ready systems.
AI model development must evolve beyond fragmented workflows. See how system-level orchestration enables scalable, production-ready AI.
Learn why AI deployment fails in production and how real-world constraints, not model accuracy, determine success.
Explore why AI at the edge delivers cost savings, speed, and reliability — and how ModelCat turns constraints into production-ready systems.
Collaboration to bring production-ready machine learning models toNXP-based devices in days— unlocking edge AI for more device businesses...
