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Artificial Intelligence (AI) is evolving rapidly and is becoming critical for businesses to remain competitive. Because of compliance challenges and to preserve confidential data, enterprise customers deploy different AI and GenAI workloads in controlled on-premises environments, such as data center, edge, and colocation. However, choosing the right platform, technology, and architecture can be challenging.
Selecting the most suitable platform for AI workloads can be a complex task, considering the multitude of available options. An ideal platform should be cloud-native, software-defined, and offer seamless integration with hardware to manage, upgrade, and monitor. The platform should also provide scalability and facilitate ease of use for both developing and deploying AI workloads.
AI applications must scale to handle large datasets and accommodate an increase in user loads. Achieving scalability becomes challenging when the architecture lacks a clear separation between control, compute, block, and object storage nodes. This design limitation hinders the system's ability to adapt to varying workload and storage requirements.
Organizations often have trouble searching through their internal knowledge base, as they are limited to text-based search, which does not offer efficient querying. RAG-based search capability offers an intelligent, context-aware search method that generates comprehensive answers to users.