COREDUMP #013: Prepping Your AI Model for the Wild: Building Edge AI Models That Work in the Real World

Edge AI is booming. From wearables and smart home devices to industrial sensors and predictive maintenance systems, teams are increasingly turning to on-device machine learning to power next-gen user experiences.

But building an ML model that works in the lab is only half the battle. Once your device is in the hands of users out in the real world, with real noise, unpredictable conditions, and hardware constraints, things can get messy.

In this episode of Coredump Sessions, we’re joined by David Tischler, Developer Relations Engineer, and Alessandro Grande, Head of Product at Edge Impulse, an Edge AI platform with over 250,000 users worldwide (now a Qualcomm company).

We talk about the hard problems product teams face when deploying ML models to embedded devices—and how to set yourself up for long-term success.

In this conversation, we cover:

• Why AI models often fail in production—and how to prevent it
• How to plan for real-world variability, noisy data, and drift
• What “observability” looks like for AI models running at the edge
• Strategies for model versioning, retraining, and shipping OTA updates
• Where Edge AI is headed—and what tooling still needs to catch up

Whether you’re building voice interfaces, gesture recognition, anomaly detection, or computer vision on-device, this episode is packed with insights from two experts who’ve seen it all.