Is there any one who has built their own Tech Stack for Generative AI with edge LLM Concept model while embedding strong enterprise data set?

Yes, IBM has been actively working on enabling enterprises to build their tech stacks for generative AI, incorporating large language models (LLMs) with a focus on edge computing and embedding strong enterprise data sets. IBM's approach often centres around a few key concepts and technologies:
 
1. IBM Watsonx
- Watsonx is IBM's AI and data platform that allows enterprises to train, tune, and deploy AI models, including generative AI. It provides tools for working with large language models and can be integrated with enterprise data sets to create more tailored and effective AI solutions.
- The platform offers capabilities for handling LLMs, including those operating at the edge, bringing AI closer to where data is generated and used. This is crucial for reducing latency and improving the efficiency of AI operations in real-time applications.
 
2. Edge AI and LLMs
IBM has been pushing the integration of AI at the edge, which means deploying AI models on devices closer to where the data is generated rather than relying solely on centralized cloud infrastructure. This is particularly important when low latency and data privacy are critical.
This means enterprises can deploy LLMs at the edge, allowing for faster, more secure, and more context-aware AI-driven decisions. Embedding enterprise data into these models allows for more accurate and relevant outputs, tailored to the organization's specific needs.
 
3. Data Embedding and Integration
IBM emphasizes the importance of embedding enterprise data into AI models. This involves integrating structured and unstructured data from various sources within an organization into the LLMs. The goal is to make AI solutions more intelligent and contextually aware of the specific business environment.
IBM's solutions often involve using data lakes, data warehouses, and other data management tools to prepare and integrate this data effectively.
 
4. Hybrid Cloud and AI
IBM’s hybrid cloud strategy supports the deployment of AI across different environments, whether it's on-premises, in private or public clouds, or at the edge. This flexibility is essential for enterprises that need to manage and process data in diverse environments while still leveraging the power of LLMs and generative AI.
 
5. Security and Governance
IBM also places a strong emphasis on security and governance when building tech stacks for generative AI. This includes ensuring that data is securely handled, models are explainable, and AI outputs are auditable. This is particularly important for enterprise applications where regulatory compliance and data protection are paramount.
 
 
In summary, IBM provides a robust set of tools and frameworks that allow enterprises to build their tech stacks for generative AI, incorporating edge computing and strong enterprise data sets. The key components include IBM Watsonx for AI model management, edge AI deployment capabilities, and a focus on data integration, security, and hybrid cloud environments.

I have no experience with doing this and do not know of anyone doing their own.