AI at the edge allows real-time machine learning through localized processing, allowing for immediate data processing, detailed security and heightened customer experience. At the same time, many enterprises are looking to push AI into the cloud, which can reduce barriers to implementation, improve knowledge sharing and support larger models. The path forward lies in finding a balance that takes advantage of cloud and edge strengths.

…In a perfect world, we’d centralize all workloads in the cloud for simplicity and scale, however, factors such as latency, bandwidth, autonomy, security and privacy are necessitating more AI models to be deployed at the edge, proximal to the data source. Read More