AI is becoming an important technology for organizations around the world searching for solutions that provide elevated levels of customer experience, process optimization, and innovation. Edge AI’s distributed and on-device nature unlocks real-time insights and process automation possibilities, with applications ranging from manufacturing and customer service, to hospitals and research labs.
Sheena Patel and Jorge Silva of Edge Impulse, an edge AI development platform, join top executives in San Francisco on July 11-12, to discuss and share their experiences on how leaders are integrating and optimising AI investments for success, while also avoiding common pitfalls. At Transform 2023, they will explain why AI must move towards the edge, the challenges of developing algorithms for edge devices, and what companies and developers should focus on to ensure success in the building and deployment of their edge AI solutions.
Edge AI requires that algorithms must run on a device with limited or constrained compute, memory and energy resources, and is especially difficult where IoT data is not formatted like traditional big data. It is critical to begin benchmarking hardware before the bill of materials is selected, and for edge AI development tools to accommodate different users from ML engineers to firmware developers. Edge Impulse is one of the few platforms providing extensive engineering support for the development of edge AI solutions, from data infrastructure, ML development tooling, testing, deployment environments and CI/CD pipelines.
Edge Impulse offers developers and enterprises a low-code/no-code approach to creating artificial intelligence algorithms and edge solutions. It is a cloud-native platform that lets you train, validate and embed machine learning models into powerful edge devices. With Edge Impulse, developers can collect data via connected sensors, create AI models right within the platform, deploy those models to connected devices, and derive better insights faster. Additionally, its AI Compiler optimises the neural networks so they can run with minimal resources, so data scientists, developers, and engineers can build cutting-edge AI solutions with confidence.
