Edge AI Revolution: Exploiting the Growing Market Opportunity for Machine Learning

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. Learn more and replay our webinar on The Cutting Edge of MLOps.

Industry at the Edge
Written by:
Miren Zabaleta

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.

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.

Mastering the Edge: best practices for effective MLOps in the Edge

The convergence of machine learning and Edge AI introduces Data Science and Engineers to unique challenges that demand a specialized skill set that goes 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.

SESSION 1: Confidential-AI: Compliance & Data Privacy through Federated Learning Owkin, Apheris.

In this session, we will discuss the impact of regulation on AI development and the future landscape of data privacy. Discover how leading organizations leverage federated learning to address critical compliance issues and data privacy issues in industries like healthcare and manufacturing. Replay

Session 2: Real AI: Handling zero latency AI in production at scale. Modzy, Barbara.

Real-time decision-making is crucial in industries such as autonomous vehicles, industrial automation, and smart grids. Learn about the infrastructure requirements for achieving zero latency AI and explore deployment tools and platforms optimized for edge AI. Discover how MODZY and BARBARA are spearheading advancements in this area. Replay

Session3: Efficient- AI: Optimization for data-intensive deployments. Seldon, Picsellia, HPE.

Scaling up data-intensive applications presents unique challenges. Delve into the world of model compression techniques and hardware accelerators like GPUs and specialized AI chips. Explore the cutting-edge approaches that SELDON, PICSELLIA, and HPE use to optimize Machine Learning Operations for data-intensive deployments. Replay

Session 4: Autonomous-AI: Reliable and secure ML to ensure business continuity. NVIDIA, Barbara.

Deploying robust, and secure ML systems for uninterrupted business operations is key in industries such as energy, oil and gas, water utility and critical infrastructures where independence from connectivity, suppliers, and changing conditions is paramount to business continuity. In this session, we will share best practices in safeguarding ML deployments in the Edge. Replay