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Artificial intelligence doesn’t exist in a vacuum. It requires a massive infrastructure behind it to make it all work — hardware, software, networking and more. Today, deploying and managing the infrastructure to power AI is an industry all to itself, as experts constantly work to develop the most effective foundations for the scalable, efficient and secure deployment of artificial intelligence solutions.
Having the right AI infrastructure is critical for the support of all stages of the AI lifecycle, whether it’s training generative AI models, managing machine learning workflows or powering enterprise AI ecosystems. In this article, we’ll explore how teams design AI infrastructures and how to put them to use in your organization to improve your AI initiatives.
Put simply, AI infrastructure is the sum of all hardware, software and networking components teams require to support AI workloads. In a sense, it comprises the skeleton, cardiovascular and pulmonary systems that give AI its ability to function. AI infrastructure is more than just servers and GPUs and the algorithms running on them. It’s the entirety of these things and much more, all working together as a cohesive whole.
In addition to the above, AI infrastructure requires things like specialized storage devices, Kubernetes management platforms, security protocols and high-speed networking interconnects. All of these are components of AI infrastructure — and all are necessary pieces of the puzzle. If one element is missing or underperforming, the entire AI ecosystem may not function at its maximum efficiency (if it functions at all). The more robust the AI infrastructure environment is, the more efficiently and effectively teams can train and deploy models.
AI infrastructure is the basis for AI operations, with its components working together behind the scenes to deliver scalable AI solutions. The vast majority of AI infrastructure implementations are cloud-based or are deployed as a service rather than existing on-premises.
Building an AI infrastructure gets started a lot like any major IT project, beginning with hardware provisioning and planning. Decision makers must select hardware with care, ensuring that it has the power and scalability to support the intense workloads that will be placed upon it. The next step is to select a software framework that supports the building, training and deployment of your AI models. From here, the infrastructure platform is able to ingest data that will be used for training and model development; managing this data is another key task of AI infrastructure managers. Finally, with the infrastructure in place, teams are ready to train, validate and deploy these AI models in the field.
We’ll talk more about each of these steps in the next section.
AI infrastructure is composed of five key elements. Here’s a look at how each of them contributes to a whole that is greater than the sum of its parts, working together to form an effective AI ecosystem.
The core differentiating feature of AI infrastructure is the use of GPUs (graphics processing units) and/or TPUs (tensor processing units) that can handle the massive parallel processing tasks endemic to AI workloads. These processors must be installed on high-performance servers loaded with memory, so they’re capable of supporting the large amount of data moving back and forth during training and inference.
The vast amounts of data used for training AI models have to be stored somewhere, which means AI infrastructure must be designed either with large on-premises file servers or (more commonly) highly available cloud-based storage. Not only must this storage be large enough to accommodate these large volumes of data, it must also be accessible at the fastest speeds. Data stored on tape drives and the like is incompatible with AI infrastructure.
As data moves back and forth among devices, the AI infrastructure must support this movement with minimal latency and the highest levels of throughput. Today’s AI infrastructures rely on some of the most advanced network topologies ever created.
Developers require software frameworks to create their AI models. This includes a range of packages that developers use to develop AI algorithms, such as TensorFlow, PyTorch, LlamaIndex, CrewAI and LangChain. Each framework has a different focus and includes the resources needed to create a specific type of AI algorithm along with the tools for managing and cleaning the data used in these processes.
Orchestration and management platforms are software systems that control the deployment of completed AI models into production. These systems include automated routines that eliminate much of the need for manual server configuration and related management activities.
How does AI infrastructure benefit your organization in day-to-day operations? These five advantages are key.
AI models can be developed on traditional hardware platforms, but this is usually slow, inefficient work. Highly scalable AI infrastructure can commandeer the resources it needs to power through the vast amounts of data processing required to complete a model quickly and get it into production.
Scaling a traditional IT infrastructure means adding stacks of storage devices and hiring the staff needed to manage them. Just as AI infrastructure can scale its processing power to meet your needs, it can also scale storage space, letting you easily add capacity for all the data your AI models will need as your operation grows.
AI infrastructure platforms are designed with security in mind from the outset. While traditional infrastructure may require constant patching with security updates, AI infrastructure is designed to withstand all types of attacks at every stage of the AI lifecycle. This also helps maintain compliance with regulations focused on AI and the data it uses.
Better scalability means fewer wasted resources and lower costs, because you only pay for the resources you use. This in turn improves the ROI of your AI program while making expenses more predictable.
Building your own AI applications like generative AI models is a complex endeavor that you can’t easily achieve within the confines of a traditional IT framework. Put simply: If you really want to explore the potential of AI, you will need an AI infrastructure on which to do it.
Ready to get started building your own AI infrastructure? Here are six key steps to get you on the right path.
No successful AI initiative has ever been launched without a solid understanding of what the organization needs now and where it wants to go. This initial planning step also includes setting budgets in addition to determining the specific problems you are hoping to address with AI.
Once you know the problem(s) you are hoping to solve, your provider can help guide you to the hardware and storage options that would be best for your specific use cases. For example, experts generally consider GPUs better for genAI training, while TPUs are often a better fit for machine learning tasks.
Different AI frameworks excel at different tasks; you can’t just pick one and expect it to handle all your AI needs out of the box. Selecting the ideal framework and its related orchestration system is a key decision that requires the input of seasoned experts who understand the nuances of each available option.
Don’t get caught playing catch-up on security. Your service provider will have countless security resources available for you from the start. Make sure you understand how they work and that they are properly implemented.
Scalability and performance under load can and should be stress-tested before a model is put into production. Spin up workloads that approximate what you expect real-world demand to look like — and then some, perhaps, to see how your AI infrastructure performs. If you’ve done all of the above properly, it should have no trouble managing very heavy workloads without choking.
Your AI workloads will not live in isolation; teams must constantly monitor them as they undergo real-world usage. If performance begins to lag, that’s your cue to reevaluate the infrastructure decisions that you’ve made and consider changes to improve performance and efficiency.
Curious how AI infrastructure works in the real world? Consider these examples.
Autonomous vehicles are the perfect example of deep learning being applied on high-performance systems, as automobiles have zero margin for error when it comes to safety. In self-driving cars, data is continuously collected from thousands of vehicles for training on neural networks, driving continuous improvement.
Consider a financial fraud detection system, which relies on high-speed, cloud-based infrastructure to capture anomalous events in real time. The infrastructure must be able to scale with consumer spending, capturing transactions seamlessly, whether it’s a slow Tuesday morning or the start of the holiday rush.
Online sellers crunch the numbers constantly to determine what’s trending and what’s running cold. This kind of analysis is an ideal use case for enterprise AI infrastructure, with rapidly produced insights allowing sellers to change course with a moment’s notice.
A scalable, secure and integrated infrastructure is essential for supporting successful AI initiatives at the enterprise level. By combining flexible technology platforms with strong security measures and streamlined workflows, organizations are able to embark on rapid experimentation, streamlined model deployment and effective management of those models, ultimately fostering innovation, enhancing operational efficiency and helping maintain compliance.
A traditional IT infrastructure of off-the-shelf servers and switches isn’t designed to provide the intensive computing power that AI workloads require. AI workloads need an environment that can combine the massive parallel computing power, scalable storage and low-latency features that make AI possible.
The 7 main areas of AI are generally enumerated as machine learning, deep learning, natural language processing, computer vision, robotics, expert systems and fuzzy logic. Each represents a special branch of AI and a particular approach to solving problems with AI techniques.
While it can be constructed on-premises, most AI infrastructure is built in conjunction with cloud service providers and data platforms, such as the Snowflake AI Data Cloud.
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