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How to do private Artificial Intelligence
Wed, 20th Mar 2024

While the potential benefits of AI speak for themselves, enterprises need a measured, strategic approach to pursue those benefits without placing their valuable intellectual property at risk. This is why many businesses are starting to build their own AI models, host those models on private infrastructure, and use only proprietary datasets to train them. This concept is known as private AI.

Many enterprises now recognise that when they feed sensitive data into public AI services such as ChatGPT, that data gets trained into the model. In turn, this means the data could be exposed to anyone who uses the model in the future. OpenAI’s own FAQ states that users should not share any sensitive information with ChatGPT, as there’s no way to delete specific prompts from a user’s history.

With private AI, you can extract business insights from your data without having to sacrifice privacy or control over that data. Read on to learn four factors you should incorporate into your strategy to help you succeed with private AI. 

Make sure private AI is right for you
First, it’s important to realise that not all businesses will be successful with private AI, particularly if they don’t start with a clearly defined vision of what success looks like for their specific situation. For businesses in highly regulated industries like healthcare and financial services, the benefits of private AI are obvious. They know they need to avoid doing anything that might place their sensitive data at risk, so private AI is a natural fit.

Businesses in unregulated industries may still be able to benefit from private AI, but the value proposition isn’t always as clear. These businesses must consider the trade-offs: both the risk of data leakage and the cost and flexibility impact of doing AI on public infrastructure. Some companies gravitate toward public cloud because they see it as an easy and cost-effective way to get the scalable compute infrastructure their AI models demand. However, accessing public cloud compute is often more expensive and difficult than expected, in large part due to high data egress fees. If you determine that the supposed benefits of public cloud infrastructure are not enough to make up for the potential risk, then you know your business is a good candidate to proceed with private AI. 

Incorporate data management into your strategy
Considering all the rapid advancement we’ve seen in AI technology during the last several years, it may be worth stepping back to consider one fundamental fact: Your AI models can only be as good as the data you feed into them. This is why effective data management is essential to private AI success. You need to account for how you’ll get the right data to the right places without delay. This can be challenging because AI infrastructure is highly distributed by default: 

  • You need to collect data from all your applications—which are likely going to be in a hybrid multicloud architecture—to feed your training models.
  • You need to deploy inference workloads at the edge (i.e., the locations where end users interact with the AI models) to ensure proximity between data sources and processing locations. This is essential because inference workloads are very sensitive to latency, and distance is the primary driver of network latency.
  • You need to deploy training workloads on core infrastructure that can provide the massive compute capacity those workloads demand. 
  • You need flexible, high-performance networking between all your different workloads so data can move quickly and reliably from the source to various processing locations.

One ideal way to build an AI-ready data architecture is by using cloud-adjacent storage. This allows you to incorporate public cloud services into your private AI strategy while mitigating the potential risks, cost and complexity. It’s like having the best of both worlds for your AI infrastructure: You’re close enough to the cloud that you can access services when you need them, but you’re also able to keep your authoritative storage environment separate from the cloud. 

This approach means that you can maintain complete control over your data—control to use it when and how you want, without worrying about it being leaked through a public AI model or getting locked into a particular cloud. Ensuring this level of control over your data is one of the hallmarks of an effective private AI strategy.

Consider your compute needs
The explosive growth of AI has led to increased demand for powerful GPU hardware. Manufacturers are ramping up to meet this demand, but even so, we’ll likely be facing supply shortfalls for the foreseeable future. Limited hardware availability could prevent you from fully realising your private AI goals. However, there are ways you can avoid this bottleneck and still get the compute capacity you need.

Many people consider “GPUs” to be synonymous with “AI hardware,” but this isn’t necessarily true. While you’ll definitely need GPUs to support your most demanding training workloads, you’ll likely be able to use readily available CPUs for your smaller inference workloads. In fact, you could even use a Bare Metal as a Service solution such as Equinix Metal to help you deploy the CPUs you need on demand without high up-front costs.

In addition, even for workloads that do require GPUs, you have options beyond deploying and managing your own hardware (after waiting months for delivery). For instance, Equinix recently announced a fully managed private cloud service in partnership with NVIDIA. This service makes it quicker and easier for customers to get the advanced AI infrastructure they need, packaged with the required colocation, networking and managed services to host and operate that infrastructure. The solution offers all the flexibility you’d expect from a public cloud solution while allowing you to maintain control over your data in a private environment.    

Plan for sustainability and efficiency
Many people are justifiably concerned that the current rush toward AI will derail the sustainability progress that some enterprises have made in recent years. It’s true that AI workloads—and training workloads in particular—can be very energy-intensive. In order to limit the carbon impact of these workloads, you need to run them as efficiently as possible.
For instance, new liquid cooling technology for data centres is inherently more efficient than traditional air cooling. It will play an essential role in cooling high-density workloads, such as AI, in an energy-efficient manner. At Equinix, we’ve been testing liquid cooling extensively for years now and have begun using it to support production workloads.

In addition, it’s important to consider how workload placement could impact sustainability. You want to place your workloads in locations where they can pull the least carbon-intensive energy from the local grid. One way to achieve this is by working with a digital infrastructure partner that has prioritised investing in renewable energy. 

At Equinix, we’re well on our way to achieving our goal of 100% renewable energy coverage globally by 2030. To help create greener grids, we’ve invested in power purchase agreements (PPAs) that support renewable energy projects in regions throughout the world. Our customers can benefit from this increased renewables coverage as they work to meet their goal of doing AI in a sustainable manner.