AI Containers™ vs. Docker & Kubernetes
AI Containers™ vs. Docker
For more information about AI Containers™, please visit the AI Containers™ page.
Containers and Docker, in particular, have been a significant (r)evolution in IT and in the way to code, test, deploy, and manage applications. It started with the Virtual Machine revolution, 20 years ago, enabling IT administrators to optimize IT infrastructure (compute, storage, and network) as well as increase availability.
Containers, often referred to as OS-level virtualization, remove the OS from virtualization, enabling Applications to run on top of other applications contained in images. This technology allowed for software-centric deployments (code, test, deployment, and maintenance), known today as DevOps.
Docker and Containers, in general, are very useful to deploy applications on IT and cloud systems; however, deploying AI Applications to embedded devices with real-time video, audio and sensors has its own set of challenges, mostly related to plumbing and configuration:
- Ingress: which imager/sensor has to be connected to an AI Application / AI Model?
- Egress: how to standardize the format of inferences and inference quality metrics from the AI Application / AI model?
- Data handling: How to resize/transform data streams, easily, efficiently, and automatically on an embedded device?
- Scale: How to deploy Edge AI without requiring expensive and hard to find software developers?
- Configuration: how to standardize the configuration of these AI Applications?
- How to provide easy access to AI Acceleration, Storage, Crypto Cores, and Vaults.
Unfortunately, Docker containers do not provide solutions to those challenges, worse they are adding an additional layer of software, using compute, and complexity lengthening development cycles. Finally, a good way to understand AI Containers™ is to compare them to Android/iOS applications. Most interactions between the device hardware and the Application is standardized (e.g., camera, microphone, etc.), AI Containers™ do the same for AI Applications.
We designed AI Containers™ to facilitate the deployment of AI Applications to the edge by System Administrators with a graphical web tool.
AI Containers™ vs. Kubernetes
Kubernetes is an open-source container orchestration system. In other words, it’s a convenient tool to orchestrate the deployment of containers on host machines, whether those are in a local data center or are in a cloud system. Kubernetes has a lot of fancy features that are heavily biased towards enterprise-class IT, such as automating deployment, scaling, and operations of application containers across clusters of hosts. Kubernetes, which is the foundation of many PaaS & IaaS systems, works with Docker and other container systems.
Deploying AI Applications to millions of embedded devices in the field with intermittent network connections (e.g., 4G/LTE, 5G, Wi-Fi), Over-the-Air (OTA) is a completely different ball game. Furthermore, as compute capability is limited and varies a lot on embedded devices, you have to understand the compute performance level required, as well as the sensor types needed to run a specific AI Application. On top of that, system administrators want to have one single interface to manage their camera networks, like deploying AI Applications as well as Firmware and OS updates.
Console is an API based web portal to let you configure & manage large deployments of AI-Driven and connected camera networks. Console enables system administrators to push AI Containers™ to edge devices, Over-the-Air. On top of that, Console allows system administrators to update OS, firmware, and camera apps “en masse” and Over-the-Air. AnyConnect offers a wealth of AI Applications in its marketplace: the AI Store™. Deploying an AI Application through the AI Store™ is almost as easy as installing an App on your phone. Console is API based and integrates with existing deployment management systems.
Summary – AI Containers™ vs. Docker & Kubernetes
| AI Containers™ | Docker & Kubernetes | |
|---|---|---|
| Solution for containerized AI Applications mostly at the edge. | ✓ |  |
| Simple and graphical data ingress management | ✓ | |
| Simple and graphical inference data egress management | ✓ | |
| Simple and graphical AI Application Configuration management | ✓ | |
| Designed for OTA deployment | ✓ | ✕ |
| Designed for OTA deployment on difficult and intermittent networks (Cellular/Wi-Fi, etc.) | ✓ | ✕ |
| Encrypted deployment | ✓ | 3rd party for channel encryption |
| Integrated Access Control management | ✓ | Optional and complex to setup |
| Automated and simple deployment of containers on new edge devices | ✓ | Requires scripting |
| Mass deployment on a group of edge devices | Yes, one click. | Yes, through Swarm, with complex configuration and clusters (designed for Datacenters). |
| Inference data gathering | Automatic | Manual |
| Monitoring AI Application’s health and resources consumption | Automatic | Requires scripting |
| Automated advisory if the device has the right hardware feature & resources | Yes, Automatically | Resource only |
| Redundancy/replication | Not needed on embedded systems, automated deployment | Through Swarm & Clusters |
| Solution for general-purpose containerized application, mostly in datacenters. | ✕ | ✓ |