AI pilot to production: challenges & solutions with HPE Private Cloud AI

[JoannStarke]

Unleash your AI potential. HPE Private Cloud AI bridges the gap to production with secure, scalable infrastructure, federated data access, and a rich AI software ecosystem. 

You did it! Your AI pilot is a success. But here's the twist: most AI projects never make it past this point.

Artificial intelligence (AI) is revolutionizing organizations, boosting productivity, improving operational efficiency and unlocking new market opportunities. From a strategy perspective these are all good goals, but realizing this potential is often fraught with challenges.

In this blog post, we’ll delve into the current state of enterprise AI and explore how to move beyond AI pilots to production with HPE Private Cloud AI.

Current state of AI enterprise

McKinsey reports that 72% of companies are experimenting with AI, but few pilots make it to production.¹ Why?

• Navigating the sprawling data landscape of hybrid enterprises hinders data discovery and accessibility
• Ensuring security and compliance across data and models is a critical hurdle
• Integrating AI pilots into existing systems and meeting performance demands in production
• The AI landscape is rapidly evolving, raising concerns about a model’s longevity

While these challenges are significant, private clouds are emerging as a key player in hybrid enterprise strategies. Offering a dedicated and secure environment, private cloud can address many of the obstacles hindering AI pilot success.

Building your own private cloud, however, is no easy task. Whether you choose a DIY approach or leverage a reference architecture, the costs and complexities can be substantial. If your business relies on Red Hat^® OpenShift^® and Apache Spark^TM, you might think building your own cloud would be straightforward. Think again. Here's why:

Massive investment: The infrastructure, hardware, and software requirements can be overwhelming.

Complex management: Managing a private cloud is a full-time job, demanding multiple point solutions and dedicated teams.

Ongoing security: Persistent security oversight, including applying patches, is crucial.

Scaling uncertainties: Scaling a private cloud can be unpredictable, limiting access to the latest features and hindering innovation.

Reference architecture-based solutions provide a solid foundation but come with pre-defined components and configurations. This can limit flexibility and make it difficult to adapt the cloud environments to specific business needs or emerging technologies.

NVIDIA and Hewlett Packard Enterprise knew a different approach to private cloud was required.

HPE Private Cloud AI

Introduced at Discover in June, our end-to-end AI solution combines infrastructure, hardware, GPUs, Kubernetes, and a comprehensive AI software ecosystem. Leveraging our deep expertise in data, analytics, and predictive AI, HPE Private Cloud AI offers the industry's most integrated solution for NVIDIA AI computing, networking, and software with HPE's AI storage, compute, and GreenLake cloud. The objective of the solution is to empower businesses of all sizes, with an energy-efficient, fast, and flexible path for developing and deploying generative AI applications.

How does this work?
HPE Private Cloud AI abstracts the infrastructure, freeing data engineers and data scientists from the complexities of daily infrastructure management. However, for IT professionals who prefer hands-on infrastructure control, the solution offers granular zero-trust security and access controls across all models, data sources, and users, all managed from a centralized dashboard. It ensures confidential and sensitive data remains on premises and under your control.

AI is data-driven, but data discovery and access can be a major time sink for data engineers, hindering their ability to focus on building pipelines.

Figure 1. Sampling of data sources available in HPE Private Cloud AI

HPE Private Cloud AI addresses this challenge by providing federated visibility and access to any type of data, regardless of its location. Imagine feeding your AI models with structured, unstructured, and object data from sources such as Snowflake, Apache Iceberg, Teradata, Delta Lake, Amazon S3, or MinIO. Need to add a new data source? Simply click the “+” sign and add it in.

By automating data access and discovery, data engineers can streamline pipeline creation by accessing both private, sensitive, and non-sensitive data across their organization.

With HPE Private Cloud AI simplifying data access and discovery, data scientists can focus on what they do best: building and refining AI models. Let’s see how data scientists can leverage this platform to accelerate their work.

It starts with a comprehensive platform for AI development and deployment that includes automation for routine tasks. Add a rich ecosystem of built-in tools and notebooks, and data scientists have the flexibility to choose the best approach for each project. Need a specific tool or application? Easily import any custom or third-party software into the platform. Containerization streamlines recovery from failed experiments, reducing downtime from weeks to hours. And the centralized platform fosters seamless collaboration across teams.

 Figure 2. Sampling of the persona based self-service catalog for AI tooling

HPE Private Cloud AI offers customizable configurations to suit various workloads. Our streamlined control plane simplifies Day 0-2 operations, manages hardware and software setup and validation, monitoring, and workload lifecycles. Tightly integrated with the AI software stack, this control plane supports ongoing operations and maintenance.

Figure 3. You’re ready for productivity with three clicks

From AI Pilots to production: overcoming challenges with HPE Private Cloud AI
While many organizations are experimenting with AI, successfully transitioning AI pilots to production remains elusive due to data challenges, security concerns, integration difficulties, and the rapidly evolving AI landscape.

HPE Private Cloud AI offers a comprehensive solution to address these challenges. By providing a dedicated and secure environment, it simplifies data access, accelerates model development, and ensures seamless integration with existing systems.

HPE Private Cloud AI streamlines the journey from AI pilot to production. With federated data access, automation, ecosystem of the most popular AI tooling, and a centralized platform, data scientists and engineers can focus on building high-performing AI models on a consistent infrastructure.

Learn more at hpe.com or go deeper by watching a recent presentation at Field Tech Days.

Joann Starke
Hewlett Packard Enterprise

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¹  The state of AI in early 2024: GenAI adoption spikes and starts to generate value, McKinsey, May 2024.