Edge AI Revolution: exploiting the growing market opportunity for Machine Learning

From intelligent forecasting in energy and predictive maintenance in manufacturing to AI-powered instruments in healthcare, the possibilities of Edge AI?seem endless. With its speed, efficiency, and security benefits, Edge AI is set to revolutionize the way businesses operate and make decisions.

By 2025, a staggering 75% of enterprise data will be created at the edge. Moreover, by 2027 deep learning will be in over 65% of edge use cases. As the volume of data continues to increase, computing is shifting towards the edge. This presents a unique opportunity for AI /ML Teams to learn and adopt best practices in implementing Machine Learning in the Edge.

Edge AI on the Rise

Edge AI is a cutting-edge technology that combines edge computing and artificial intelligence (AI) to bring advanced computing capabilities to the edge of the network. It is a revolutionary new concept that combines AI and Edge computing.

Unlike traditional cloud-based computing, where data is transmitted to a central server for processing, Edge AI algorithms are processed locally, either directly on the device or on a server near the device. With its speed, efficiency, and security benefits, edge AI is set to revolutionize the way businesses operate and make decisions.

An increasing number of enterprises are recognizing the advantages of implementing machine learning (ML) in the edge. This shift is driven by various factors, like the need to minimize latency for autonomous equipment, reduce expenses associated with cloud data ingestion and storage, or because of a lack of connectivity in remote locations where highly secure systems can’t be connected to the open internet.

Mastering the Edge: best practices for effective ML from optimisation, and deployment to monitoring

The convergence of machine learning and Edge AI, presents Data Scientists and ML Engineers, with new challenges that require a specialized skill set beyond traditional machine learning engineering.

This includes considerations such as optimizing model performance for edge devices, ensuring robust connectivity and data management, addressing security and privacy concerns, and leveraging suitable deployment frameworks and tools amongs others.

Re-experience "The Cutting-EDGE of MLOps" webinar and get insights into building compliant, efficient, and real-time edge AI solutions with OWKIN Apheris Modzy Picsellia , Seldon 惠普企业服务 英伟达 and Barbara .