Cerbrec is a visual deep learning framework designed to create AI solutions through drag-and-drop or natural language. According to information on its official website, Cerbrec also provides a service that connects multiple sites and operates them as one network, enabling distributed capacity to be sold and run as a single tenant-ready service.
Uses: Cerbrec acts as an operating partner, integrating sites from construction and renovation through ongoing operations. It serves as the operational layer that turns owned properties into tenant-available compute capacity and connects and operates sites as one network, providing the scale and reliability tenants need.
Key capabilities:
- Connect sites into one tenant-ready data center network.
- Convert properties at different stages of development, such as offices or storage buildings, into data centers.
- Serve as the operational layer that turns properties into compute capacity.
- Combine sites into a single product for easier leasing by coordinating redundancy and reliability.
- Adapt to each site, using part or all of the space while minimizing structural changes and disruption.
- Reduce new construction and new site development, minimizing noise, light, and excess energy use.
- Support local jobs across construction, electrical, mechanical, technology, maintenance, and operations.
- Tenants gain aggregated capacity across an edge data center network, SLA-compliant operational reliability, and scalable operating expenditure.
- AI agents designed for data analysis and intelligent operations.
How to use: According to information on the official website, Cerbrec works by connecting and operating sites as a network. Users can discuss with the Cerbrec team about connecting sites into one tenant-ready network. Detailed steps on the specific use of the visual deep learning framework, such as creating AI solutions through drag-and-drop or natural language, are not currently provided.
Applicable scenarios:
- Turn suitable sites into tenant-ready infrastructure, repositioning existing assets for higher-value use and better returns.
- Lease capacity as a single network, which is easier to lease than any individual site.
- Maintain flexibility in properties and adapt to each site.
- Minimize environmental impact and community impact.
- Create local economic opportunities.
- Support larger compute demands and future expansion.
- Coordinate workloads and redundancy to support uptime and SLA requirements.
- Grow data center compute capacity without increasing operating costs at the same rate.
Limitations: Specific features, fees, pricing, platform, results, or usage steps of the visual deep learning framework are not currently provided on the official website.
