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As enterprise AI initiatives take off, IT leaders are under increasing pressure to use AI to maximize competitive advantage. However, embarking on this journey presents hurdles for even the best. While in some ways AI is just like other workloads, it also comes with unique challenges:

• Data management complexities
• Data privacy concerns
• Lack of in-house AI expertise
• The need for increased power densities and advanced cooling capabilities
• The need for high-performance access to clouds and services
• Distribution of infrastructure to enable edge inference
• Need for increased network capacity to handle data transfer to and from AI workloads

Imagine a pharmaceutical company that tasked its CTO with figuring out an AI solution to help them transform drug discovery. Such a project would require the company to process vast quantities of molecular data as well as data from scientific databases and clinical trials—much of which is sensitive, proprietary information they couldn’t risk putting in the cloud. To succeed with such an ambitious project, the CTO knew he’d need to use a hybrid infrastructure model and work with IT industry leaders who specialize in delivering AI-ready solutions that are outside his team’s wheelhouse.

As organizations like this one begin to explore AI opportunities, they’re often looking for help executing their vision. At each step of the AI journey—from narrowing the list of potential AI initiatives to identifying the right datasets to choosing a model to figuring out the infrastructure requirements and where to put that infrastructure—you can find trusted partners to help you bring your AI goals to fruition. If you’re in a highly regulated industry or have special data requirements, there are solutions that enable you to do AI securely and efficiently, while protecting sensitive and proprietary data.

Like any major IT initiative, AI comes with its challenges, but the right partners can help you tackle them and unlock new value with AI. By using purpose-built AI infrastructure in highly interconnected private data centers, you can take advantage of all the opportunities AI brings while protecting your data.

Build a foundation with a clear data management strategy

Data is the lifeblood that powers AI, and data management has become increasingly complex for enterprises. This was true even before AI went mainstream, but now it’s a reality companies can’t avoid. The data needed for an AI project typically comes in many different forms and is located in different places, from object storage in public clouds to databases in private storage around the world. It’s important to clean these datasets prior to use with model training, fine-tuning, or retrieval augmented generation (RAG).

Because of , as data accumulates, there’s a gravitational pull of other resources to wherever the data is. Enterprises end up putting other applications and data where their largest datasets are. But this can lead to high costs and inefficiencies down the road—especially if all your data gravitated to the public cloud.

Data privacy and sovereignty regulations further reinforce the need for a thoughtful data strategy that helps you protect sensitive information and comply with regulations. You need such a strategy before you even launch an AI project. From there, you can figure out the infrastructure requirements and where to put that infrastructure.

An  data architecture model is imperative to maintain control of your data while incorporating flexible cloud services. With this approach, you keep custody of your datasets in a core location while maintaining proximity to clouds and service providers and the right compute resources. This data strategy prepares you to manage the unique requirements of distributed AI workloads, such as the need to quickly move data from edge locations to the cloud and back for AI inference.

Think about the right AI infrastructure and where to put it

When you’re ready to finetune an AI model for basic inference or deploy RAG to generate higher-quality insights, it’s important to understand the infrastructure requirements for your project. This includes high-performance compute, robust data storage and management, and flexible networking that supports the high bandwidth requirements of AI. From there, you can determine where to put that infrastructure, whether it’s in your data center, in the cloud, or in an  facility.

In recent years, many enterprises have been reconsidering which workloads are best suited to the cloud. The trend of repatriating workloads to  has organizations revisiting the value of hybrid multicloud architectures that allow them to use the optimal mix of public and private infrastructure for their needs. As it turns out, AI, too, can take advantage of hybrid infrastructure.

Many enterprises default to using public cloud for AI because of the availability of AI services there and the speed of deployment. But there are other options for your AI compute—and they often come with sizable benefits, including flexibility and greater control of your data, as well as the ability to avoid vendor lock-in.

Take advantage of private AI solutions

Because of the sensitive nature of the pharmaceutical company’s data, their CTO likely wouldn’t want to launch the drug discovery AI project in the public cloud. Instead, he would need to explore cloud adjacent private infrastructure to support the initiative.

 refers to the use of private infrastructure that allows you to customize AI solutions with your proprietary data. With this approach, you put an authoritative copy of your data on private infrastructure and then have the flexibility to access the data from cloud-native, private, and SaaS deployments.

Private AI offers  over public AI:

• Greater data security and control
• Optimized performance
• Cost optimization and reduced egress charges
• Helps solve the data gravity problem inherent to keeping critical data in a cloud environment

Like this pharmaceutical company, many organizations have data that simply can’t go to the cloud and requires a different place to live. And nearly every company wants to keep control of their data given that it’s often their most valuable resource. Even for enterprises planning to use cloud-based foundation models, a time will come when it’s necessary to tune those models with proprietary data. With private AI, it’s possible to build and deploy AI solutions on privately hosted infrastructure located next to all the major cloud on-ramps and privately connected to them via scalable, high-speed interconnection for optimal performance. So, you don’t need to fret if you and your teams aren’t experts on AI networking.

Rely on partners with vast AI experience

We know that businesses want to focus on all the things they can do with AI rather than worrying about how to do it. Many enterprises lack in-house AI infrastructure expertise and are wary of investing in all new AI hardware in their current data centers. For such companies, the  enables organizations with a business-ready AI portfolio of platforms, software, and services, where they can design and deploy their AI strategy and use cases that move the business forward. Companies can choose a use case, such as code generation or a digital assistant, and then receive custom AI configuration recommendations.

Equinix and Dell Technologies have a long history of working together to help joint customers access IT solutions that address their needs. Both companies have a wealth of AI expertise to offer joint customers:

Dell Technologies delivers AI infrastructure that’s already tailored to AI workloads, such as Dell Data Lakehouse, a data storage and analytics appliance to support effective data management for multicloud organizations, and Dell AI Factory with NVIDIA, an end-to-end enterprise AI solution designed to simplify and accelerate AI adoption. With Dell’s deep technical expertise in data storage and high-performance computing, enterprises are equipped to implement their AI strategies quickly and effectively.

Deploying Dell infrastructure in Equinix AI-ready data centers gives organizations the security and control of private AI infrastructure while incorporating access to cloud-based services and providing high-speed interconnection to partners and service providers, all with liquid cooling capabilities in data centers that prioritize sustainability. With the robust Equinix network of 260+ high-performance data centers around the world, enterprises can do AI inference in the right locations for them and easily scale to new locations as needed.

Dell AI Factory with NVIDIA deployed at Equinix

More organizations are entrusting their AI futures to partners who specialize in delivering AI solutions. Doing so relieves the pressure of trying to tackle the more complicated and infrastructure-intensive aspects of AI alone. With Dell AI Factory with NVIDIA deployed at Equinix, you can take advantage of all that private AI offers without having to dedicate your sole focus to overseeing a new deployment. Once you tackle the first hurdle of figuring out what you want to do with AI, you can focus your energies on AI innovation and leave the infrastructure to Equinix and Dell.

Our joint private AI solution empowers you to protect your data while accessing clouds, networks, software solutions, remote users, and sustainability innovations in facilities across the globe.

Learn more about deploying Dell AI Factory with NVIDIA at Equinix in our .