Imagine a large regional healthcare provider that wants to make more data-driven decisions aligned to their mission of providing compassionate care. They start exploring AI initiatives to enable more personalized patient experiences and better patient diagnostics. The organization’s IT leaders have already adopted a hybrid multicloud architecture and experimented with some AI services in the cloud.
However, as soon as they start to assess the infrastructure requirements for successful large-scale AI solutions, it’s clear that the healthcare system’s aging network is a limitation. They’re already struggling with application latency, so launching new AI applications would put further strain on the network. Clearly, it’s time to optimize their network for the future.
Networking might not be the first thing companies think of when planning their AI initiatives, but it’s crucial. AI requires massive quantities of data to be transferred to and from applications very quickly. In technical terms, AI workloads require:
- Low latency for sensitive AI applications during both the development and deployment stages
- High throughput, for two reasons—first, because model tuning and development runs finish faster leading to increased time to market, and second, because the transfer of huge datasets across the network reduces time to production
- High reliability, since many AI applications require near-instantaneous processing
Traditional network architectures—like the one the healthcare provider built decades ago—weren’t designed to move data over the shortest, most efficient path. And now, the rapid growth of data and AI solutions is increasing network traffic every year, putting further strain on aging infrastructure. 2023 saw a 25% rise in global internet traffic,[1] and there will no doubt be much more in the coming years as the AI boom continues.
Network modernization initiatives deliver a range of business benefits, from improving network reliability and security to cost optimization. For companies eager to adopt more AI solutions, the time to update and optimize your network is now.
Why networking is so important for AI
In addition to the large volumes of data for AI, the data isn’t always generated where the AI processing happens, and you can’t always bring AI processing to where data is generated, or vice versa. Networking is the way to overcome this challenge.
You might wonder, isn’t that the point of edge computing? Yes, it is—and AI applications are increasingly using edge computing to process data closer to where it’s generated to reduce latency and bandwidth usage, and to make AI systems more responsive and efficient. However, networking is still vital for connecting edge devices and ensuring seamless data flow between edge and core infrastructure. Edge computing can reduce the volume of traffic but doesn’t eliminate the need for good networking. Another consideration is moving the inferencing from the edge to the metro level. In some instances, this location optimizes the tradeoff between latency and the costs to backhaul the data to the metro level. But an optimized network is a requirement to fully utilize this architecture. It’s still crucial to build your network in a way that accounts for the requirements of AI.
The healthcare provider in our example has more than 20 hospitals and 500+ outpatient facilities in a region that spans three U.S. states. They have a centralized data center in a major city, but they collect data from dispersed locations across their entire architecture. When transferring diagnostic and patient data for processing, their centralized network architecture regularly becomes a bottleneck and keeps them from adopting AI for mission-critical applications—exactly where the benefit could be the greatest.
With the right network architecture, however, the healthcare provider can accelerate data transfer, optimize for AI application latency, reduce the costs to transfer data and open up new possibilities for the use of AI in diagnostics and personalized medicine.
Network modernization helps you tackle AI head-on
When it comes to AI, traditional network architectures—which weren’t designed and optimized for distributed workloads—can present several challenges, such as:
- Insufficient network capacity
- Traffic routing that adds latency and may even lead to unnecessary costs
- Inability to scale quickly as projects grow or customer demand increases
To address these problems, organizations have begun rearchitecting their enterprise networks to be more decentralized. In some ways, this is necessary due to global expansion and the increased use of multicloud. Enterprise networks have become more complex than they used to be. Many companies, however, haven’t gone through this transformation yet and are unprepared for the networking requirements of AI projects.
Network modernization initiatives might include several of the following:
- Utilize network architecture strategies to account for increasingly distributed environments
- Optimizing cloud connectivity to improve application performance
- Implementing software-defined networking (SDN) to improve speed and agility
- Beefing up network security to protect enterprise data and increase reliability
- Deploying SD-WAN to simplify network management and orchestration
A modernized network architecture is more adaptable, making it easier to scale network services up or down as your AI projects progress. It can be also designed with more built-in resiliency, so you’re prepared for unexpected interruptions. And it will better prepare you to adopt emerging technologies like AI and ML.
Multicloud networking—A critical AI success factor
Many companies turn to AI services in the cloud for their earliest AI projects, since they’re widely available and great for getting started with AI. Low-latency access to those services is an important AI success factor. Multicloud networking, therefore, is becoming a critical discipline for organizations to master as they launch their AI journeys.
We know enterprises are already using multicloud models to help them access specific capabilities, optimize costs and increase their resilience by diversifying vendors. According to research from TechTarget’s Enterprise Strategy Group, 94% of organizations are using multiple unique public clouds.[2] They’ll likely use this same model for AI projects since their data is already being generated and stored across cloud environments. Not to mention—clouds and new AI service providers are racing to invest in developing their AI capabilities. A multicloud network strategy ensures no matter which solution is chosen, it’s flexible and future-proof with the ability to adapt and change as the AI needs of the business change.
Most organizations using multiple clouds are, in fact, using hybrid multicloud models since some workloads require greater privacy, security and control. There may be certain datasets or analytics tasks that shouldn’t happen in the cloud—or they may be looking for ways to optimize costs by using some private infrastructure.
If you’re including private AI in your digital strategy, multicloud networking is no less important. You’ll want to keep those private datasets close to the clouds and use multicloud networking across your hybrid architecture to keep everything connected and communicating seamlessly.
Modernize your network for AI with Equinix
When the healthcare provider was ready to upgrade their infrastructure, they opted to deploy virtual network functions (VNFs), including upgraded virtual routers, SD-WAN devices and firewalls. They deployed edge nodes in their facilities across the region to collect data on location. And to optimize their multicloud networking, they are using a virtual cloud-to-cloud routing solution. Now, the organization is ready to implement AI-powered diagnostics and personalized treatment plans.
If you’re ready to explore AI and haven’t updated your network yet, now’s the time. Equinix—with its global footprint of data centers—is helping customers rearchitect their networks for greater efficiency and resiliency. Being on Platform Equinix® gives you access to a vibrant ecosystem of cloud, networking and security service providers as well as an emerging ecosystem for AI-related services. Our on-demand networking and connectivity services are great for building adaptable networks that can easily scale to address the needs and requirements of your AI projects.
Network modernization should be a priority for every organization. If you’ve already updated your network, you’re likely in a good place to launch AI initiatives today. But if you haven’t, you have no time to lose. In the era of AI, companies simply can’t put off optimizing their networks any longer.
If you’re looking to modernize your networking to prepare for AI, multicloud networking is a great place to start. Learn more about architecting a multicloud network by downloading our planning guide.
[1] Sascha Brodsky, Internet traffic soars in 2023, with generative AI a standout trend: Report, Computerworld, December 13, 2023.
[2] Enterprise Strategy Group Research Report, Distributed Cloud Series: The Mainstreaming of Cloud-native Apps and Methodology, July 2023.
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