Recent years have seen significant evolvement of deep learning and AI capabilities. AI solutions can augment or replace mundane tasks, increase workforce productivity, and relieve human bottlenecks. Unlike traditional automation, these solutions include cognitive aspects that used to require human decision making. In some cases, deep learning has proven to be even more accurate than humans in identifying patterns and therefore can be effectively used to enable various kinds of automated, real-time decision making.
The advanced analytics team at Intel IT recently implemented an internal visual inference platform—a high-performance system for deep learning inference—designed for production environments. This innovative system enables easy deployment of many DL models in production while enabling a closed feedback loop were data flows in and decisions are returned through a fast REST API. The system maximizes throughputs through batching and smart in-memory caching and can be deployed as either a cluster or standalone node.
Moty Fania explains how Intel implemented the platform and shares lessons learned along the way. To enable stream analytics at scale, the system was built in a modern microservices architecture using cutting-edge technologies, such as TensorFlow, TensorFlow Serving, Redis, Flask, and more. It is optimized to be easily deployed with Docker and Kubernetes and cuts down time to market for deploying a DL solution. By supporting different kinds of models and various inputs, including images and video streams, this system can enable deployment of smart visual inspection solutions with real-time decision making.
Moty Fania is a principal engineer and the CTO of the Advanced Analytics Group at Intel, which delivers AI and big data solutions across Intel. Moty has rich experience in ML engineering, analytics, data warehousing, and decision-support solutions. He led the architecture work and development of various AI and big data initiatives such as IoT systems, predictive engines, online inference systems, and more.
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