World Congress 2025 • Aug 20, 2025 • Session details

From Code to Motion: Building an Autonomous Hat-Hunting Robot with Kubernetes & ML

Daniel Brintzinger

To solve the nightmare of disconnected edge deployments, engineers built an autonomous, hat-hunting robot. Discover how standard Kubernetes and GitOps push machine learning models from code to physical motion.

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#1 about 3 min

Challenges of application edge deployment and containers

How DevOps approaches and containers enable application delivery to disconnected edge devices.

#2 about 7 min

Hardware constraints and technology stack for the robot

The physical components and software tools used to build the autonomous robot.

#3 about 8 min

Training an object recognition model with OpenShift AI

Using Jupyter Lab and a machine learning pipeline to train an object detection model.

#4 about 4 min

Deploying and testing the AI inference service

How to expose a trained model as a secure REST API for application consumption.

#5 about 8 min

Building the robot control application using Eclipse Che

Creating a Python application within a cloud-based development environment to interact with the AI service.

#6 about 3 min

Live demonstration of the autonomous hat-hunting robot

Executing the control application to observe the robot scanning for and navigating toward the target.

#7 about 2 min

Continuous deployment to edge devices using Argo CD

Managing the physical robot as a registered Kubernetes cluster to automate configuration and code rollouts.

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1:54 min

Speaker background and open source Kubernetes edge computing projects

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2:35 min

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1:44 min

Navigating hardware constraints and edge computing challenges

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3:17 min

Deploying parallel models on custom hardware with Edge Impulse

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1:09 min

Deploying algorithms and AI models to edge production

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1:50 min

Overview of the Edge AI ecosystem and tech stack

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