From months to hours: rapid AI deployment with HPE

[JoannStarke]

HPE Private Cloud AI streamlines AI development by simplifying complex tasks to unleash innovation. Start seeing results in hours, not months. 

AI has the potential to revolutionize industries but bringing it to life often involves significant hurdles. Setting up the right infrastructure, choosing the right tools, and preparing data can be a complex and time-consuming process. After these initial steps, you'll still need to carefully integrate AI tools with your existing infrastructure and ensure security, compliance, and controlled access for a wide range of users. This intricate process can easily stretch to months.

With HPE Private Cloud AI, you can start seeing results in just hours, not months.

Eight-hour AI — from box to productivity

HPE Private Cloud AI is a turnkey solution developed with NVIDIA that’s designed to accelerate your AI initiatives. Forget months of installing and configuration, this pre-built solution can have you up and running in hours.^[2] Our solution comes pre-configured with HPE and NVIDIA servers, storage, networking, AI software and models. No more tedious setups — just plug in and go!

Here’s how easy it is:

• Unbox and roll the unit into your data center
• Integrate it into your environment
• Connect to the HPE GreenLake platform, and you’re ready to roll.

HPE Private Cloud provides a fully integrated AI infrastructure stack with optimal server, storage, and network configurations — including NVIDIA Spectrum-X Ethernet networking, HPE GreenLake for File Storage, and HPE ProLiant servers with NVIDIA accelerated computing.

Three different NVIDIA-Certified system configurations are available (small, medium, and large) to scale across a broad range of inferencing, retrieval-augmented generation (RAG), and fine-tuning workloads in the enterprise. Each of these configurations were designed based on NVIDIA Enterprise Reference Architectures, leveraging tested and validated technologies to help ensure that AI workloads run at peak performance.

In addition to simplifying IT administration, we've also streamlined the AI user and developer experience.

Continuous innovation, delivered

The rapid proliferation of specialized AI tools and frameworks has created a fragmented landscape. While this diversity offers flexibility, it can also hinder seamless integration and slow down AI initiatives.

Figure 1. Enterprises today face a complex maze of AI tools

To overcome these hurdles and help organizations harness the full potential of AI, we built-in an ecosystem of AI tools and technologies including seamless access to the NVIDIA AI Enterprise software platform — a comprehensive suite of tools and technologies designed to accelerate production AI deployments. This includes NVIDIA NeMo Retriever, NVIDIA NIM microservices, and NVIDIA Blueprints. Additionally, HPE's robust tools and models are integrated with NVIDIA AI Enterprise to provide a cohesive, end-to-end solution for enterprise AI innovation. Deploying NVIDIA NIM microservices or an open-source model serving framework such as Ray, is as simple as accessing the model catalog and clicking “open.” 

Figure 2. Built-in evergreen ecosystem of AI tools and frameworks supported by HPE.

HPE and NVIDIA rigorously test and regularly update these solutions to deliver seamless integration across the entire private cloud stack. Without needing to worry about complex integration and manual updates, you can focus on innovation. 

With these powerful AI tools and frameworks, developers can create generative AI (GenAI) chatbots that engage in natural, informative conversations, enhancing user experiences.

GenAI: a powerful tool, a complex challenge

GenAI, a specialized form of AI, leverages large language models (LLMs) to learn from vast amounts of text data to generate new, original content. While LLMs are powerful tools, they can sometimes produce inaccurate or irrelevant responses, often termed "hallucinations." These inaccuracies can stem from various factors, including poor data quality, limitations in the model's understanding of context, or poorly constructed prompts.

To enhance the accuracy and relevance of LLMs, many organizations are turning to RAG. RAG has become so popular that it's often the starting point for customers' AI journey, particularly for applications involving sensitive and confidential data.

RAG chatbots are poised to revolutionize how we interact with information, making them the most sought-after GenAI application. However, building robust RAG chatbots is a complex endeavor.

Key challenges in building RAG chatbots:

• Data ingestion and preparation: Gathering and preparing diverse datasets for analysis.
• Embedding: Converting textual data into numerical representations suitable for efficient search and retrieval.
• Prompt engineering: Crafting precise prompts to guide the model's understanding of user queries and generate accurate responses.
• Query processing and response generation: Effectively processing user queries and generating relevant and informative responses.

HPE Private Cloud AI has taken these complex steps and turned it into a no code three-step process that streamlines your path to market faster.

Figure 3. RAG Essentials simplifies creation of RAG-based chatbots into three steps.

RAG essentials

Creating RAG-based chatbots is as simple as 1, 2, 3. 

Step 1:   Determine the type of model you want to use. HPE Private Cloud AI provides a choice of models that can be accessed by choosing “select another.”

Step 2:  Select data sources by directly accessing multiple distributed data formats: object, structured, and unstructured. These data sources can be outside the HPE Private Cloud AI domain, such as public cloud, Snowflake or Teradata. Access to all sources is dependent on user permissions established by IT.

Step 3:  RAG Essentials offers default infrastructure configurations. For more experienced users, these settings can be adjusted to meet specific requirements. 

Clicking 'Deploy' triggers a background process that automatically sets up your chatbot, following a predefined workflow.

After your chatbot is created and added to the application catalog, it is available on-demand. Clicking 'Deploy' initiates the same backend processes to provision and configure the chatbot.

Want to build a custom chatbot? Our pre-built RAG components, accessible via API endpoints, provide a powerful foundation. Data scientists and AI developers can use these components to create tailored AI solutions with ease.

Revolutionize your workflow, reduce costs
Developing and deploying AI and GenAI solutions can be hindered by the complexities of infrastructure setup, data preparation, and model training. HPE Private Cloud AI addresses these challenges through an easy-to-use platform that significantly reduces the time and resources required to bring AI solutions to market.

HPE Private Cloud AI revolutionizes AI operations, freeing up data scientists to focus on high-value work and driving significant cost savings. Let’s consider a hypothetical scenario.

Quantum Leap Technologies spends a significant amount of time and resources on data preparation, model creation, training, and deployment. By leveraging HPE Private Cloud AI’s automated tools and pre-built models, they could achieve the following time and cost savings.

While this scenario is grounded in California data scientist salaries, it's important to note that compensation can fluctuate based on various factors, including geographic location and industry.

Senior Data Scientist (fully loaded)[2]	$300,000/year
Avg Salary/48 weeks/40-hours	$156.00/hour
10% efficiency per data scientist from automated processes and workflows 4 hrs. per week x 48 x $156	$30,000 per data scientist

By leveraging HPE Private Cloud AI, Quantum Leap Technologies' team of 10 data scientists can achieve significant cost savings of $300,000 annually. This technology empowers their team to work more efficiently, reducing the need for additional hires.

By adopting HPE Private Cloud AI, you can empower your team, optimize your resources, and drive innovation. Contact your local HPE sales representative to learn more about HPE Private Cloud AI.

Learn more:  https://hpe.com/private-cloud-ai

Joann Starke
Hewlett Packard Enterprise

twitter.com/HPE_Ezmeral
linkedin.com/showcase/hpe-ezmeral
hpe.com/software

[1] 8 hours deployment based on initial customer experience    

[2] Occupational Outlook Handbook for California, USA, 2024.