By Michael Corrado, Worldwide Marketing, HPE Private Cloud AI
It will revolutionize the financial services industry, accelerating tasks such as the review of complicated and sometimes subjective financial documents like loan applications, while keeping our money more secure with advanced fraud detection and prevention capabilities. Healthcare will see personalized treatments and rapid diagnosis while lifting the weight of administration for doctors with powerful virtual assistants that can leverage expansive medical knowledge combined with private patient data. Industries like retail and the public sector will be enabled to improve their customer experiences and efficiency through automation and dynamic forecasting, which reduces risk and drives increased customer satisfaction.
While most enterprise organizations want to leverage AI to extract more value from their data, improve their customers' experiences, or streamline and improve their existing operations, the complexity and challenges associated with AI implementation and management have hindered widespread adoption. The truth is that most enterprises need help scaling models into production.
According to IDC, more than 80% of enterprises are experimenting with GenAI initiatives. However, Forbes reports that fewer than 1 in 10 AI projects get beyond pilot and into production, much less realize return on investment.
This means that most organizations are spending time, engineering resources, and money on something that’s unlikely to impact their balance sheet positively. This is largely because AI is not a single workload but rather a complex integration of microservices, AI models, IT resources, and data sets. To achieve tangible outcomes, AI workloads require a range of computational resources, infrastructure, tools, and data sets, ideally combined with a simplified user experience and thoughtfully integrated software resources.
The AI software landscape is not only massive and complex but at the current rate of innovation, its near impossible to keep up and make informed and confident choices. Once you navigate the complexities of implementation and initial orchestration of workloads, most enterprises also must account for governance and regulatory compliance, data privacy and security, as well as the potential costs of experimenting in the public cloud.
What you really need is the freedom to start small with a guaranteed outcome and the flexibility to experiment and grow efficiently, with access to the most current AI technology available. This is not to say that public cloud services don’t serve a role in a successful AI strategy. Public cloud does help many first-time AI adopters with early AI experimentation and allow for scaling unpredictable workloads, but a balanced hybrid cloud approach is necessary for enterprise-level production. Building private clouds specifically designed for AI allows for more cost-effective utilization of AI resources, predictable costs with flexible, subscription-based, fixed-cost models, consistent workloads, regulatory compliance through physical control over the data, and increased productivity of AI teams. Ultimately, this leads to faster time to value from AI investments.
This is why the future of AI lies in hybrid cloud solutions.
Here at HPE, we think a gap still exists in addressing the long-term needs of our customers, as we aim to simplify the end-to-end AI experience and maximize the AI productivity of their AI and data science teams.
How do we do that? We start with the problems our customers are having succeeding with AI. Do any of these challenges sound familiar?
“We're struggling to enable AI and IT ops teams to successfully drive AI pilots to production and provide value quickly.”
We took from this that our customers need something that is ready to run out of the box. They need one-click, self-serve access to simplify connecting and orchestrating data sets, they need help accelerating AI deployment and they need to optimize performance and GPU utilization.
“AI environments are complex for IT to operate while ensuring security, governance, and compliance.”
AI can be complex enough without adding additional risk, which is why a hybrid cloud strategy with enterprise-grade confidence and control is needed to help our customers to protect and control their data. Done right, we should allow our customers to innovate faster while staying compliant, auto-scale as AI demand grows, enable enterprise-wide collaboration, and secure sensitive data and models while beingprotected by a single source of support on all of the integrated components.
"We need flexibility to start small and grow and adapt to future AI opportunities.”
Our customers need the flexibility to grow but shouldn’t be expected to know the future. This is why we need to give all customers the choice of scalable solution starting points for all the most common AI use cases like inferencing, RAG, LLM, fine-tuning, etc. We have to protect their investments by maintaining a consistent operating model while working to integrate future HPE, NVIDIA and open-source AI innovations, and we need to give them the flexibility to consume and manage on their own terms with financial and management options so they can choose what is right for their AI goals.
Now that we understand some of the most common AI challenges, let's consider some of the most common needs among most enterprise organizations.
- I need support for inference, fine-tuning, and RAG AI workloads.
- I need to increase the productivity of my AI teams.
- My business demands enterprise control with robust data privacy, security, transparency, and governance.
- I need the ability to start fast, remain open and consume flexibly.
- I want assurance that my hardware and software configurations are pre-validated to run the workloads I want to run.
If any of these needs sound familiar, you are in the right place. HPE Private Cloud AI is a turnkey AI private cloud that helps you:
- Accelerate data scientist productivity to get pilots to production faster.
- Deliver a cloud experience in a private environment, offering the ability to rapidly experiment and scale
- Keep your data accessible, private and under your control at all times.
HPE Private Cloud AI, part of the NVIDIA AI Computing by HPE portfolio, is an enterprise private cloud solution that delivers a unique, cloud-based experience to accelerate innovation and manage enterprise risk from Gen AI. Engineered to support inference, fine-tuning and RAG use cases and leverage data across on premises and public cloud, it can be deployed on premises in colos, at the edge or in data centers.
Managed through HPE GreenLake cloud, it gives AI and IT teams powerful tools to experiment and operationalize AI while keeping data private and secure and within enterprise and regulatory guidelines. Built on turnkey, full-stack solutions validated and by HPE and NVIDIA, it helps customers expand and add new AI capabilities and offers the flexibility to consume and manage as demand within the enterprise grows.
Unlike full-stack AI solutions based on reference architectures that can take months to plan, build and deploy with professional services, HPE Private Cloud AI is turnkey, deployed in hours, cloud-managed, and ready to use by AI personas and IT operations teams, providing productivity in minutes.
See how it works at HPE Private Cloud AI
Learn about the leading AI use cases in your industry:
Michael Corrado is a senior world wide marketing manager for HPE and has specialized in sales and marketing strategies for HPE Services, HPE GreenLake and now leads the marketing efforts for AI and Private Cloud. Michael lives on Long Island, New York, with his wife and two sons.
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