Difference between revisions of "Edge AI SDK"
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•<u>[https://genai-studio.gitbook.io/docs GenAI Studio]</u>: Facilitates cost-effective creation, evaluation, and integration of custom LLMs on-premises. | •<u>[https://genai-studio.gitbook.io/docs GenAI Studio]</u>: Facilitates cost-effective creation, evaluation, and integration of custom LLMs on-premises. | ||
| − | •<u>[ | + | •<u>[https://ess-wiki.advantech.com.tw/view/Edge_AI_SDK/InferenceKit Inference Kit]</u>: Enables rapid optimization and assessment of efficient AI runtimes compatible with embedded operating systems. |
•<u>[https://campaign.advantech.online/en/DeviceOn/index.html Orchestration Platform]</u>: Provides efficient management of AI model and application updates across extensive edge deployments, integrating MLOps for streamlined operations, powered by [https://campaign.advantech.online/en/DeviceOn/index.html DeviceOn]. | •<u>[https://campaign.advantech.online/en/DeviceOn/index.html Orchestration Platform]</u>: Provides efficient management of AI model and application updates across extensive edge deployments, integrating MLOps for streamlined operations, powered by [https://campaign.advantech.online/en/DeviceOn/index.html DeviceOn]. | ||
Revision as of 09:57, 17 January 2025
Advantech’s Edge AI SDK offers a comprehensive platform designed for seamless edge AI development. With pre-configured hardware and perfectly tuned software, it provides a plug-and-play experience, enabling cost-effective customization of LLMs, efficient toolkit compatibility, and effortless management of large-scale edge deployments. Simplify your journey to AI innovation with tools that ensure reliability, scalability, and ease of use.
Advantech's Edge AI SDK encompasses three main components:
•GenAI Studio: Facilitates cost-effective creation, evaluation, and integration of custom LLMs on-premises.
•Inference Kit: Enables rapid optimization and assessment of efficient AI runtimes compatible with embedded operating systems.
•Orchestration Platform: Provides efficient management of AI model and application updates across extensive edge deployments, integrating MLOps for streamlined operations, powered by DeviceOn.
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