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Ubuntu Blog: How to deploy AI workloads at the edge using open source solutions
- AI workloads are driving new opportunities, from virtual assistants in healthcare to telco router optimization in remote locations.
- Open source solutions, including MicroK8s and Charmed Kubeflow, are providing greater flexibility and security for edge AI scenarios.
- Canonical, NVIDIA, and Lenovo can help companies bring AI capabilities to rugged, remote data sources by considering a purpose-built, pre-validated infrastructure stack.
- The integrated architecture is designed to efficiently handle large datasets and for specific AI workloads, enabling a faster experimentation and scaling process.
- Canonical's open source infrastructure stack includes Ubuntu Pro, a streamlined approach to managing Kubernetes containers, and Charmed Kubeflow, a distribution of Kubeflow for Kubernetes environments.
- Lenovo ThinkEdge servers leverage the NVIDIA EGX platform to provide powerful performance capabilities for AI workloads at the edge.
- The validated reference architecture offers developers and researchers a faster, more accessible path to AI initiatives.
- The deployment process includes installing the Canonical software components on the ThinkEdge SE450 server and creating AI experiments using the NVIDIA Triton inference server.
- The architecture provides accelerated computing, scalability, and security capabilities for edge AI, ultimately leading to reduced operational costs and more predictable outcomes.
- Companies are increasingly turning to open infrastructure solutions for edge AI, as they offer faster iteration and experimentation, scalability, and secure, optimized hardware and software stacks.
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