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1. Manufacturing Company
The system architecture for manufacturing company:
The Butler project addresses the digitization challenges faced by small and medium-sized enterprises (SMEs) lacking resources for transformation. The goal is to create an accessible app that equips SMEs with tools to enhance operations and competitiveness in today's digital era.
Project Objectives
The Butler aims to provide SMEs with a comprehensive solution to:
1) Visualize Production Data: The Butler will offer user-friendly data visualization to help SMEs monitor and improve production processes efficiently.
2) Simplify Knowledge Access: The app will streamline access to operation manuals and guidelines, facilitating swift troubleshooting and minimizing downtime.
3) Navigate Standards and Regulations: The Butler will assist SMEs in understanding and adhering to industry standards and regulations, ensuring product quality and compliance.
4) Market Insights: The app will offer market research insights, enabling SMEs to make informed decisions and develop effective marketing strategies.
5) Employee Training and Testing: The Butler will provide training modules and testing features to enhance employee skills and knowledge.
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Benefits for SMEs: The project offers SMEs numerous advantages:
1) Affordability: The Butler provides cost-effective digital solutions tailored to SMEs' budget constraints.
2) Ease of Use: The user-friendly interface ensures simple adoption and utilization of the app's features.
3)Efficiency: By centralizing resources, The Butler reduces time spent on information retrieval and training, boosting productivity.
4) Competitiveness: Access to insights, standards, and market data empowers SMEs to stay competitive and informed.
The Butler is a crucial step toward SME digital transformation. By providing tailored tools, the project empowers SMEs to embrace digitalization, improve efficiency, and succeed in a digital business landscape. This initiative envisions a future where SMEs can leverage digital solutions to foster growth and competitiveness.
2. Low-latency AI inference on Edge Cloud
EmpoweredgeAI–ChallengesattheEdge
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At the core of this architecture is Shifu, chosen as a short-term solution due to the developers’ familiarity with it. Shifu, positioned on the IaaS layer, is a Kubernetes-native, production-grade, protocol & vendor-agnostic IoT gateway. Shifu serves as a middleware layer, bridging applications and IoT devices by abstracting device data and functionalities into REST APIs. This configuration supports flexible deployment options, allowing Shifu to be deployed at the edge for device twins or in the cloud based on user needs. Typically, the control plane for Shifu is deployed in the cloud. It's important to note that Shifu depends on Kubernetes for its operations.
Alternatively, YoMo, situated YoMo is a AI elastic framework for AI agent, which will help AI agents build geo-distributed edge nodes infrastructure and AI API gateway, posioned on the PaaS layer, offers a different approachbring low-latency performance for every AI agent. YoMo is an open-source Large Language Model (LLM) Function Calling Framework designed specifically for building geo-distributed AI applications. Unlike Shifu, YoMo does not rely on Kubernetes, providing more flexibility in certain deployment scenarios. Depending on the use cases, users may choose either Shifu or YoMo to work on their edge AI workloads.AI agents developers can choose YoMo (Near edge) or Shifu (Far edge) based on these factors shown below.
- scalability (Number of users, number of devices etc.,)
- Type of application (Gaming, Video streaming, web content, social media etc.,),
- Latency requirement
- Throughput
- Entity that manages the application (Enterprise, Telco, Cloud Service provider etc.,)
- Security constraints
Looking ahead, depending on demand, alternative solutions such as EdgeX and Fledge may be considered to either replace or supplement Shifu. The architecture also incorporates a scalable AI Elastic Framework, which includes an AI API gateway, various Edge AI applications, and components for smart elastic computing and intelligent time-sharing scheduling.
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