The future of AI is at the edge, but teams need a better way to build edge AI applications for scale.

In this webinar, we'll discuss efficient, scalable approaches for building AI applications at the edge. We'll discuss a better approach for deploying ML models on fleets of edge devices, and why an infrastructure centric architecture approach to deployment is wrong. We'll explore architectures that support managing models across a fleet of edge devices as well as complex data connections, and discuss resource constraints, latency requirements, and network connectivity considerations. We'll also discuss the benefits of remote model updates, versioning, and performance monitoring at the edge. Finally, we'll provide some real-world examples of teams building edge-AI systems at-scale.

Whether you're a data scientist, ML engineer, or industry professional, this webinar will equip you with valuable knowledge and best practices. Join us to transform your approach to building edge AI systems for scale.