X
NetApp
Field Validated Design
The rise of Large Language Models (LLMs) is reshaping industries, yet not every business has the resources or expertise to build their own foundational models. Fine-tuning and RAG (Retrieval-Augmented Generation) technologies overcomes this limitation and are increasingly popular among businesses looking to leverage LLMs. These technologies provide flexibility by allowing customers to refine existing LLM models with domain-specific data or augment pre-trained models with proprietary information, thereby enhancing accuracy and reliability. However, concerns surrounding data governance, compliance, and privacy present significant barriers for enterprises seeking to adopt these AI techniques.
DataNeuron, a platform specializing in customized Large Language Model (LLM) solutions, has joined forces with NetApp, renowned for its Intelligent Data Infrastructure.
This partnership aims to revolutionize the deployment and scalability of LLMs in enterprises, and address key concerns surrounding LLM integration, including data security, privacy, customization, and scalability.
Overview
01
Data Curation
02
Model Lifespan and Selection
03
No-Code Pipelines
04
Data Security and Privacy
05
Intelligent Data Infrastructure
06
Robust and Responsible Data Management
Testing Environment
The Testing Environment conducted showcases the seamless integration of DataNeuron's platform with NetApp's Intelligent Data Infrastructure, providing organizations with a robust solution for LLM implementation. We experimented on different workflows of the DataNeuron platform deployed on the NetApp Intelligent Data Infrastructure and NVIDIA GPUs
DataNeuron Workflows:
DataNeuron platform supports three no-code and automated workflows
LLM and GenAI:
Prompt/Response Generation, Validation and Fine-Tuning.
Classical NLP:
Multi-label and Multi-class classification and NER.
Information Retrieval:
RAG and Playground/Q&A Interface
Model Customization
Model Customi-zation
Testing Environment:
Our Testing Environment capitalizes on NetApp's robust data infrastructure, deployed on Google Cloud Platform (GCP), seamlessly operating in serverless cloud environments. To increase the performance of our language model (LLM) pipeline, we have deployed on NVIDIA Tensor Core A100 GPU.
To optimize resource utilization and streamline data access, we incorporated load balancers into our setup. These balancers intelligently distribute incoming traffic across kubernetes clusters, minimizing latency and maximizing compute efficiency.
For efficient data management and storage, we rely on NetApp ONTAP Storage Volumes (Extreme) via GCP. These fully managed file storage solutions provide reliability and scalability for our extensive datasets and knowledge base.
To seamlessly integrate components, we utilize the NFSv3 protocol to mount NetApp volumes onto our NVIDIA A100 GPU instances. This configuration ensures smooth data accessibility and operation throughout our pipeline, enhancing the overall efficiency of our Testing Environment.
Minimum Compute Requirements/Operating System:
Operating System:
Linux Ubuntu (22.04 LTS)
GPU:
NVIDIA A100 80GB
CUDA Version:
12.2
NetApp Data Engineering Solutions for DataNeuron platform:
Snapshot:
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These features are available through the NetApp DataOps Toolkit, a python library that makes it easy for developers, data scientists, and data engineers to perform numerous data management tasks & streamline AI workflows. These features bring value to the deployment of real-time Generative AI models and help address data challenges from the edge to the data center to the cloud.
*We have used the GCP python library to enable these features in this POC.
Conclusion : DataNeuron + NetApp:
We successfully integrated and tested all the workflows of DataNeuron platform on the NetApp and Nvidia platform.
About DataNeuron:
DN represents the next frontier in LLM and Generative AI solutions. With a focus on innovation, quality, and efficiency, DN is positioned to disrupt the market and set new standards for scaling LLMs.
Contact Details:
Bharath Rao
bharath@dataneuron.aiPrakash Baskaran
prakash@dataneuron.aiNetApp:
Contact Details:
Balbeer Bhurjee
balbeer.bhurjhee@netapp.comShinil Vaish
shinil.vaish@netapp.comReferences:
Generative AI and NetApp Value:
https://docs.netapp.com/us-en/netapp-solutions/ai/wp-genai.html#netapp-capabilitiesPrivate RAG:
https://www.netapp.com/blog/private-rag-unlocking-generative-ai-for-enterprise/Ready to build your Generative AI:
https://www.netapp.com/blog/ready-to-build-your-generative-ai/