Cloud Data Platform Space --- Multiple partnerships
Differentiation: "While others offer data lakes or vector databases, Knowledge Graph Neural Network (KGNN). provides a knowledge-centric approach. It understands the meaning of your data, not just the data itself." Utilize Ai at the edge on Power 10 MMA chips
"Empower your Understanding in Real Time"
Equitus.ai's Knowledge Graph Neural Network (KGNN) can significantly enhance the security, transparency, and time-to-value for IBM Cloud users of Oracle, SAP, and VMware by facilitating seamless connections to IBM Granite, Knowledge Catalog, and Watson X. :
KGNN's ability to process data at the edge on IBM Power 10 MMA chips, without GPUs, improves security by reducing data movement and maintaining data sovereignty3. This addresses key ethical considerations and builds user trust by keeping sensitive information local.
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KGNN can ingest and structure raw data from various sources, including Oracle, SAP, and VMware environments, creating a semantically rich, machine-readable format13. This unified approach accelerates data preparation for IBM Granite, Knowledge Catalog, and Watson X. -
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By leveraging IBM Power10 servers' Matrix Math Accelerator, KGNN enables efficient AI inferencing at the edge17. This reduces latency and speeds up decision-making processes. -
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KGNN's ability to produce AI-ready, structured graph data facilitates easier integration with IBM's AI tools3. This streamlines the connection between existing systems and new AI capabilities.
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KGNN can preprocess and contextualize data from Oracle, SAP, and VMware systems, making it more readily consumable by IBM Granite for advanced analytics and AI model training. -
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By structuring data into knowledge graphs, KGNN can enrich IBM's Knowledge Catalog, improving data discovery and governance across the enterprise. -
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KGNN's graph-based data representation can provide Watson X with richer context, enhancing its natural language processing and machine learning capabilities.
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KGNN can help optimize SAP workloads running on VMware in IBM Cloud by providing real-time insights and improving data accessibility8. -
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For Oracle deployments, KGNN can enhance disaster recovery solutions by providing rapid data analysis and decision support4. -
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KGNN's edge processing capabilities can facilitate seamless data integration across multi-cloud setups, including Azure, Google Cloud, and AWS6.
By leveraging KGNN, IBM Cloud users can achieve greater debuggability through transparent data processing, increased user trust via localized data handling, and improved ethical considerations through easily scrutinizable AI operations. This holistic approach significantly reduces time-to-value while maintaining high security standards.
Citations:
- https://nas01.tallpaul.net/wordpress/2024/10/equitus-ai/
- https://blogs.vmware.com/cloud/2019/09/05/ibm-vmware-make-mark-one-kind-joint-innovation-lab/
- https://equitus.ai/kgnn-knowledge-graph-neural-network/
- https://www.ibm.com/new/announcements/ibm-cloud-reference-architectures-unleashed
- https://equitus.ai/2024/05/equitus-ai-shines-at-sof-week-2024-empowering-defense-and-commercial-organizations-with-advanced-ai-solutions/
- https://www.rackwareinc.com/sap-migrations-ibm-cloud-accelerate-with-rackware
- https://newsroom.ibm.com/Blog-New-IBM-Power-server-extends-AI-workloads-from-core-to-cloud-to-edge-for-added-business-value-across-industries
- https://community.sap.com/t5/technology-blogs-by-members/run-sap-on-vmware-in-ibm-cloud/ba-p/13551459
- Debuggability: When something goes wrong, a transparent system allows for quick diagnosis and course correction (a luxury not afforded to more black-box systems).
- User Trust: Openly demonstrating the reasoning of an agent encourages adoption and reduces user skepticism.
- Ethical Considerations: A transparent agent is also an ethical agent since it can be easily scrutinized and modified as necessary.
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