How Software Containers Accelerates Edge AI Deployment and Integration
TinyML is revolutionizing the way we develop and deploy machine learning algorithms in small embedded devices with limited computing power. Despite the plethora of tools available for TinyML development, integrating these algorithms into a larger system can be a daunting task. From managing shared computing resources and memory, to synchronizing events and seamlessly integrating with existing code, developers face numerous challenges.
Enter MICROEJ VEE, the tiny software container that brings the benefits of cloud-native solutions like Docker and Kubernetes to TinyML. With MicroEJ, you can simplify the integration and deployment of AI at the edge, without any interruptions to the device’s operation.
The replay is introduced by MicroEJ’s Chief Product and Strategy Officer Semir Haddad in collaboration with TinyML to explore the benefits of using software containers for AI at the edge. Get ready to simplify your AI journey and take your TinyML applications to the next level!
What you will learn:
- How integrate and deploy TinyML in a real product?
- Overview of the concept and origin of software containers and how they can be adapted for use at the edge
- How can software container be made Edge compliant even if the technology was originally designed for the cloud?
- Example of a standard software container for Edge ML and more detail about the concept and the implementation
- Live AI demo with Au-Zone Technologies partner, showcasing the benefits of MICROEJ VEE software containers for Edge AI deployment.