Deploying AI on the edge requires more than just shrinking a cloud model. Success comes from designing algorithms with the constraints of embedded environments in mind—where memory, compute, and power are scarce resources. In this session, we’ll explore the full stack of optimizations that make AI practical at the edge: from collecting the right data and deriving features with solid mathematical grounding, to selecting efficient neural architectures, applying quantization, pruning, and compression techniques, and ensuring that every computation contributes valuable signal to predictions.
Attendees will learn actionable strategies to:
- Improve data quality and feature selection to reduce wasted compute
- Choose lightweight neural networks tailored for embedded hardware
- Apply quantization and other model compression techniques effectively
- Balance accuracy, latency, and power trade-offs in real-world deployments
Whether you’re an embedded developer, AI engineer, or systems architect, this webinar will equip you with practical tools to unlock performance and efficiency gains for your edge AI projects.