DataNeuron LLM Studio powered by NetApp

As AI makes inroads into almost every industry around the world, businesses face new challenges. Which generative AI technology is best suited for our environment? How can we get the most out of our data? Just how easy is it for us to implement GenAI? 

Technologies like retrieval-augmented generation (RAG) and fine-tuning large language models (LLMs) have been around for a while. RAG enables organizations to build a knowledge base using their own private, sensitive data, which serves as a vector database and interfaces with LLM models for reasoning, providing accurate information based on a private knowledge base. This technique gives businesses a more reliable, accurate, and precise response to their prompts, while also saving significant cost by eliminating the requirement for extra training on a cluster of GPUs.  

On the other hand, businesses that are interested in domain-specific LLMs use domain-specific data to improve foundational LLMs. This method fine-tunes the models by using domain data, altering some parameters and weights of generic LLMs to obtain the desired result. This approach involves some training of the existing model, which requires more processing capacity. 

By integrating the best aspects of both technologies, businesses can develop a vector knowledge database with their own protected data. This database enables them to generate automated prompts and responses, which they can use to tailor the foundational LLMs to produce extremely accurate domain- and business-specific responses. 

DataNeuron is a trailblazing venture-backed startup that scales LLMs and GenAI use cases through an automation-based SaaS platform. This cutting-edge, no-code platform offers a comprehensive suite of AI solutions, including LLM data curation (prompt generation/annotation and structured/unstructured RAG), LLM fine tuning, and model management.   

The solution's core feature is the ability to seamlessly select and integrate the most suitable LLMs available on the platform, optimizing customization for specific tasks and domains, simplifying deployment, and streamlining model lifecycle management. Organizations that use this platform benefit from substantial cost and time savings while achieving comparable accuracy to state-of-the-art models, all with 95% automation. 

NetApp's intelligent data infrastructure powers the DataNeuron platform with essential speed, data governance, and security, crucial for the effective adoption of GenAI and LLM use cases. The advanced features of NetApp® ONTAP®, such as Snapshot™ copies and FlexClone® technology, significantly enhance data management throughout the AI data pipeline, including model baselining, traceability, and the maintenance of always-available datasets and vector databases.  

DataNeuron enhances workflow agility and efficiency by employing Snapshot technology, enabling users to easily revert to previous versions of the project's VectorDB volume for review or restoration. It also enables model benchmarking workflows. Within the DataNeuron platform, Snapshot integration spans both the front end and the back end, facilitating workflow versioning. This feature helps users to leverage storage capacity more efficiently while benchmarking and experimenting with GenAI and LLMs.   

With NetApp FlexClone, DataNeuron facilitates seamless experimentation by allowing users to create multiple workflows within each project, test data with different configurations, and generate clones for each workflow, enabling simple selection of the best RAG model. 

 
With the proven high availability of NetApp ONTAP, data is always available across hybrid and multicloud environments. 

DataNeuron’s partnership with NetApp simplifies, secures, and reduces the cost of developing and scaling the GenAI pipeline. To learn more about the field-validated design, click here.