How to Simplify Your AI Path to Production with CDW and HPE

More organizations are readily experimenting with AI, but many need help scaling their AI initiatives. We’ve summarized the three most prominent obstacles organizations may face when using AI in production.

Although AI applications heavily rely on organizational data to produce valuable outcomes, how this data is accessed is crucial. The AI system should be able to differentiate among public, private and sensitive data assets based on the use case it serves.

For instance, consider a customer service chatbot deployed by a bank. If it’s designed to answer queries around policies and offers (public data), it shouldn’t be allowed access to customers’ account information, which is sensitive and private.

Not understanding this distinction leads to three concerns for organizations planning to implement AI.   

• Privacy concerns: Does the AI system uphold privacy regulations such as PIPEDA and ensure that personally identifiable information (PII) isn’t leaked to malicious actors?
• Traceability concerns: Can the responses of the AI system be tracked, understood and explained easily?
• Security concerns: Is the data being fed to AI inherently safe from scenarios like cyberattacks, model theft or unauthorized access?

As organizations build their first prototype, they often discover that their data storage, integration and security mechanisms aren’t fit for AI.

As per our Canadian Hybrid Cloud Report, only three percent of organizations state that their data infrastructure was ready to handle similar AI concerns.

Even before introducing an AI model, much groundwork must be put into data encryption, privacy-first design and AI guardrailing. This requires several costly, slow and tedious changes at the architecture level.

Let’s say a company wants to move its customer records from a public cloud system to an on-premises one for AI consumption. Migration alone may involve a large number of resources and require reconfiguration before the AI accesses the data.

The second major obstacle is the manageability of AI-ready computing power. AI projects require specialized compute hardware such as GPUs, TPUs and AI-optimized chips to meet the performance benchmarks of AI workloads.

Even if your organization isn't locally training AI models, it may still need specialized hardware for inferencing workloads or vectorizing documents for retrieval-augmented generation (RAG). 

Using a framework such as an OpenAI API may be easy because the API provider does the processing. However, most enterprise systems require a more governable computing infrastructure, which is usually challenging to manage.

Key challenges related to AI infrastructure

• Talent requirements: Dedicated IT professionals are needed to run and manage AI servers, storage and networking due to the prevalence of proprietary tools.
• Complex integration: Servers need to be set up and integrated to allow for high-performance and low-latency processing, which is usually complex.
• Low customization: It isn't easy to obtain the right-sized solutions configured to an organization's unique needs based on their AI journey.

As AI systems are deployed inside an organization’s framework, the flow of data across applications, sites, virtual infrastructures and networks needs to be tightly governed.

It may seem like a double-edged sword to IT administrators. At one end, they need to open up the pathways for AI systems to consume data, but they also need to ensure that by doing so, they are not leaving any loopholes.

Data migration errors can arise when dealing with disparate systems such as multicloud environments or public-private converged solutions. In a bad case scenario, a privately hosted AI application may get more access than required to a public cloud storage bucket if the policies are misconfigured.

To prevent data migration challenges, organizations need centralized alternatives such as a unified control plane that allows them to manage multiple systems under one umbrella. However, as per the Canadian Hybrid Cloud Report, the adoption of such technologies has been low. Only 36 percent of surveyed Canadian organizations use unified commercial cloud management.

Although such alternatives can improve data handling, it’s still a decision that needs to be weighed against the ROI of the AI project. Some organizations may find it too complex to introduce new technology well before they can achieve foreseeable returns.

To overcome the aforementioned obstacles, organizations need solutions that can consolidate technical requirements and simplify management.

Our partners at HPE offer HPE Private Cloud AI, which simplifies AI development for organizations by bringing the core AI components onto a unified platform and reducing the complexity involved in the AI lifecycle.

Co-engineered with NVIDIA AI computing, the platform offers networking, software, models and storage in one consumable solution. Organizations can use the components they need the most to streamline their AI pilot.

Key features that simplify AI development

• Seamless private cloud infrastructure: Offers a fully integrated, pre-tested and AI-optimized private cloud, reducing the complexity of managing data bottlenecks such as privacy and traceability.
• Customizable infrastructure: Obtain the right AI-ready hardware that’s suited to the scope and scale of your AI initiative with options for small inferencing servers or large RAG with fine-tuning capabilities.
• Simpler control plane: Provides a cloud-like experience with self-service tools, enabling better security and scaling of AI projects.

To further reduce production challenges, the solution also features a set of built-in offerings that are useful for building and hosting AI applications, mentioned below.

AI models

The solution comes with a library of AI models, including large language models (LLMs) and models for specific industry use cases, enabling enterprises to accelerate AI deployment. Native access to models reduces the efforts required to validate and load open-source models.

AI software

The software layer includes tools from HPE and NVIDIA, such as NVIDIA AI Enterprise software and HPE AI Essentials, to expedite data pipelines, model development and deployment. This software reduces dependency on third party tools and improves compliance.

AI infrastructure

The HPE infrastructure already includes AI-ready hardware such as HPE ProLiant Gen12 servers, HPE AI storage and NVIDIA GPUs, providing robust computing power and storage solutions for AI workloads.

With in-house access to the tools, the platform and easier customization, organizations can start their path to production with fewer challenges than before.

CDW partners with HPE to help organizations obtain the most optimal HPE hardware and design cloud-ready solutions for streamlined AI development.

Our in-house HPE technology experts and strong industry partnership for HPE computing, HPE Greenlake and infrastructure services allow us to assist you in your AI journey.

We drive value by helping you connect, protect, analyze and act on all your data and applications, from edge to cloud, wherever they live.

As you plan to bring AI to your organization, CDW and HPE make it simpler to:

• Build the right solution: Talk to our solution architects about the approach that will work best for you.
• Access the right technology: Get the technology that delivers performance, from servers to software.
• Follow the best practices: Mitigate foreseeable risks, address concerns and overcome known challenges immediately.