Many ambitious enterprises are exploring practical ways to leverage artificial intelligence. Developments in the space are showing business leaders that this technology has many serious applications—but those who want to harness those applications for business growth need robust AI infrastructure to get there.

A business that invests in strong AI infrastructure can put itself in the best position to seize new opportunities and tackle emerging challenges. Conversely, proceeding without such a foundation is a recipe for encountering obstructive performance bottlenecks and long-term scalability limitations.

In order to build your business into an AI powerhouse, you first need to understand what exactly AI infrastructure entails. After that, you can form a solid idea of how to create a framework to implement AI effectively, and sustain and expand it as needed.

In this article, we’ll explore the concept of AI infrastructure, explain how it differs from traditional IT systems, and outline the key benefits a robust AI foundation can offer. We’ll also detail the essential components of effective AI infrastructure and provide a clear step-by-step guide to building a setup that aligns with your business objectives, compliance requirements, and budget constraints. Let’s dive in.

What is AI infrastructure?

AI infrastructure encompasses all of the hardware, software, and related resources committed to the purpose of developing and using artificial intelligence. It requires high-performance computing, exceptional scalability, massive amounts of data, and careful oversight from skilled experts.

At the core of every AI infrastructure configuration you’ll find processing devices capable of training and running sophisticated machine learning models—such as large language models (LLMs), which are commonly used to provide high-level natural language processing and generation.

These processing devices have typically been GPUs (graphics processing units). While these are still the dominant choice, TPUs (tensor processing units)—chips designed specifically to deliver efficient machine learning—are becoming more popular and gaining traction, particularly in specialized AI applications.

Additionally, AI infrastructure also includes various supporting systems designed to help handle data storage, project management, compliance governance, integrations, user interfaces, and anything else that’s needed for its performance. Some companies package these supporting systems into AI platforms, which means they bundle everything together for easier deployment and use, rather than requiring businesses to set up each component separately.

The importance of a solid AI infrastructure foundation

Every business has unique goals and challenges, so there’s no universal standard for what’s included in AI infrastructure or how exactly it’s assembled. Regardless of the chosen configuration, an adequate AI infrastructure foundation must keep varied processes and technologies working together to achieve maximum performance from AI tools.

Since competing without AI technology is becoming difficult in many markets, it’s hard to make a compelling case (particularly in the long term) for not building your own AI infrastructure. Without it, businesses may struggle with critical applications, including predictive analytics, customer support, at-scale automation, and risk assessment.

For proof of the importance of AI infrastructure, consider the industries investing heavily in AI. Financial institutions are using AI to spot potential fraud by poring over vast quantities of transactions and flagging abnormalities in real time. Healthcare organizations, meanwhile, are implementing systems to further key processes such as diagnostics and treatment planning, helping to reduce stress on their workers and deliver superior outcomes.

In short, AI infrastructure solutions provide the foundation for enterprise businesses to maximize AI’s transformational potential. Businesses that prioritize AI infrastructure are likely to be far better positioned to move with the fast pace of these times.

AI infrastructure vs. IT infrastructure: Why does AI need dedicated infrastructure?

What sets AI infrastructure apart from the IT infrastructure you’re already familiar with? Shouldn’t a solid IT foundation have everything you need to achieve excellent AI tool performance? In reality, AI workloads need specialized resources and system arrangements to accomplish their goals, and this can be a burden on the legacy network. Below are the key reasons why dedicated infrastructure is essential for AI.

AI requires specialized processing power

Conventional IT infrastructure mostly uses CPUs (central processing units) designed for general computing. While these CPUs are excellent at sequential processing, low-latency tasks, and single-threaded operations, they tend to lack performance when it comes to large-scale AI training.

AI infrastructure, however, uses GPUs and TPUs optimized for the complex parallel processing that’s so important for AI algorithms. GPUs were originally developed to accelerate 3D rendering by taking care of the many distinct processes needed to show realistic virtual worlds (with lighting and physics being particularly tricky). In contrast, TPUs are proprietary chips developed by Google for deep learning workloads, particularly tensor computations in neural networks.

‘Scale’ is a vital word here: the demands placed on machine learning systems are extraordinary. The difference between using standard-issue IT infrastructure and AI infrastructure is significant time savings; a well-optimized multi-GPU or TPU cluster can train massive models in days.

AI needs scalable data storage

AI applications demand and generate enormous volumes of data, and that burden grows as they prove their value and are joined by investment in further AI tools with wider remits. IT infrastructure never needed to scale so dramatically, at least not until it had to deal with big data analytics, IoT, and high-performance computing (HPC). Relational databases would build up with system data, but it would never become as weighty or essential as AI data.

AI’s storage demands are different—not just larger. They require high-throughput access to unstructured data (e.g. images, video, text) and real-time data streaming, which traditional relational databases aren’t designed for.

Dealing with this demand requires one of two solutions: The first is building the type of data storage facility common for the largest of enterprise businesses, and the second is using an AI-geared cloud storage solution with the capacity and capability to keep data moving without major bottlenecks.

The former makes sense for highly regulated industries or workloads requiring ultra-low latency, but the latter could be easier to justify for some businesses.

AI depends on low latency and high throughput

Most of AI’s most notable applications (handling expansive real-time analytics, steering autonomous vehicles, monitoring cybersecurity, etc) need to achieve low latency (the gap between calling for data and receiving it) and high throughput (the amount of data that can be delivered in a given amount of time). Common IT setups aren’t nearly that demanding, so IT infrastructure is relatively limited.

A high-priority AI system needs a fit-for-purpose Ethernet, InfiniBand, or NVLink connection (in most cases, Wi-Fi won’t suffice), plus software developed to operate quickly and reliably, and seamless integrations with systems supplying or receiving data. In high-stakes settings such as autonomous driving or hospital systems, even the slightest delay can have serious consequences.

AI benefits hugely from software customization

Think about putting together a spreadsheet. Does it help to implement customizations within your spreadsheet software of choice, such as macros, keyboard shortcuts, app integrations? In many cases it can help, but the options are limited.

With AI tasks, though, specialized customizations could drastically improve both the results and the pace of the process. Investing in software stacks using machine learning frameworks and languages devised to aid data analysis can lead to better resource use, easier deployment, and superior iteration.

Achieving these customizations isn’t easy, which is why AI infrastructure must be carefully developed over time—that is, rather than something that’s completed and put into place as a static solution before any work can be done. Only through incremental tweaking can you achieve an AI foundation that fully suits your business and supports you in everything you’re trying to do. It’s a constant process of optimization and updating.

AI demands enhanced security and compliance

For optimal results, AI systems must draw upon as much high-quality data as possible, and that data must be diligently protected to ensure total compliance with privacy and safety regulations. This is especially important given that generative systems' inherent variability heightens the risk of disclosing private and sensitive data.

Independently from industry and location, organizations must see protection of private data as a paramount concern, especially in fields like diagnostics or cybersecurity. To keep things secure, AI infrastructure calls for AI-specific solutions employing strong security measures such as role-based access and end-to-end encryption. It also demands data sourcing and storage processes that follow the law by auditing data use regularly, ensuring strict compliance with current regulations (such as GDPR and HIPAA), and promising adherence to future regulations as and when they’re implemented.

AI infrastructure and operations fundamentals

If you hope to effectively implement and manage AI, you should first form a solid understanding of the four core components of AI infrastructure. Here’s a breakdown of the basics.

Data storage and processing

The more efficiently you can manage large data volumes, the more productively you’ll be able to train and update your AI tools. Robust AI infrastructure pairs data warehouses and data lakes—which are vast pools of unsorted data—with systems intended to make data management easier, such as compression algorithms capable of saving large amounts of space, or deduplication programs developed to keep training data clean and effective.

For maximum impact, this data must be easily moved between systems and models, which also calls for sophisticated stream processing and analysis. A robust pipeline and distribution process will deliver excellent responsiveness and scalability while minimizing downtime.

Computing resources

Having the right computing resources is critical for performing the complex calculations involved in AI workloads. Modern GPUs (which often have some AI functionality baked in) and TPUs are ideal due to their relative proficiency at parallel processing. CPUs are still necessary for related processes, but they shouldn’t do the bulk of the heavy lifting for AI infrastructure.

HPC clusters are commonly made available through the cloud, allowing many businesses, even those below the enterprise level, to access high-level AI systems. They allow dynamic resource allocation with accordingly variable pricing, allowing supply to scale neatly with demand and provide consistent value.

Machine learning frameworks

As machine learning has matured, developers have worked on frameworks that provide essential tools and guidance to accelerate the development and deployment of AI models. Taking advantage of these frameworks allows faster model creation, testing, implementation, and optimization.

The extensive software libraries and pools of community-led resources within machine learning frameworks help experts stay up-to-date and give newcomers the support they need to start getting results. The frameworks could also contain pre-built environments for experimentation and debugging, many of which can be rolled out directly, and thus rapidly, especially within cloud environments.

MLOps

MLOps, standing for machine learning operations, is an assortment of principles and practices developed to streamline machine learning workflows. Where frameworks contain software and informational resources to help people use them for model development and deployment, MLOps concentrates on aiding the use and improvement of models once they’re in place.

Keep in mind that even the most finely built AI model won’t return much value if users are not familiar with it. MLOps guides teams to design reliable workflows and effectively collaborate on model maintenance and iteration, leading to consistent progress and operational transparency that encourages course correction when performance wavers.

How to build AI infrastructure in five simple steps

These five steps can help get you started towards making steady progress on your AI infrastructure.

1. Clearly define your objectives and budget

First, you must outline your business objectives, specify how AI can help you further those objectives, and set out your expected outcomes of implementing it.

Make sure to identify whether your objectives are feasible before you invest. Similarly, due to the costs of AI infrastructure, be sure to factor in both short-term and long-term ROI. Set-up, software, processing, maintenance, scaling, and possible talent to be hired are only a few of the areas to consider when planning ahead.

2. Choose between cloud, hybrid, or on-premises deployment

To choose the best approach for your business needs, consider how you’ll deploy your AI infrastructure. Can you meet your needs with the flexibility, convenience, and scalability of cloud-based AI infrastructure? Or do you need the enhanced security, data protection, and rich customizability of an on-premises private AI deployment?

A cloud-based approach can bring more flexibility, allowing scalability with little setup costs. However, if you’re working with very sensitive data in a heavily regulated field, a more tailored approach and private secure AI infrastructure may be the goal. There’s also the alternative of a hybrid approach, bringing benefits from both cloud and on-premises together.

3. Invest in suitable hardware, software, and networking solutions

Selecting the right combination of AI infrastructure solutions is crucial; components must fit together well to ensure smooth cooperation. When it comes to hardware, begin by picking the GPUs and TPUs that best suit your unique needs. For instance, if you need rapid real-time functionality, confirm that your selected processing tier can accommodate that. Also make sure to pick high-speed storage (NVMe SSDs) and sufficient memory for your project. For software, choose your preferred machine learning framework, as well as your favorite MLOps tools, and containerization.

What you should choose for networking depends on your workload requirements and your chosen deployment model. If you’re anticipating large-scale data transfers and/or real-time processing, you’ll need high-speed Ethernet, InfiniBand, or NVLink for low-latency data transfer. Smaller projects could work with Wi-Fi, although it is not recommended.

4. Establish data privacy and security measures

When it comes to data security and compliance, it’s better to be overzealous. Aim to establish rigorous privacy controls and monitoring processes so attempted intrusions can be caught early.

You should also put a special focus on the team tasked with data privacy and security measures. Everyone should have well-defined responsibilities and be trained accordingly. Note that the training should extend past AI-specific elements to cover general cybersecurity. Even a superior set of security protocols can do little if a key member of your AI management team fails to secure their account and allows a malicious actor to access it.

5. Implement, maintain, and monitor

Once all hardware, software, and networking components have been chosen, it's time to implement the infrastructure. This process includes setting up the physical or cloud-based systems, configuring the environments (such as machine learning frameworks, storage solutions, and containerization), and making sure that the system is fitting your specific use case. Integrating your AI infrastructure with your existing business infrastructure and any external component, such as data sources or third-party applications, is paramount to helping avoid headaches down the line.

Proper maintenance of a successful AI infrastructure also entails regular updates. These are important both for compatibility and security reasons, as these technologies can evolve quickly. Additionally, make sure your system is fit for seamless scaling to keep up with your business. As demand increases, extra computing resources, storage, and network bandwidth may need to be added, and data quality needs to be assessed regularly to ensure continued compliance.

Real-time monitoring can be used to track performance metrics, monitor security risks and compliance, and verify that everything is running smoothly.

AI infrastructure: The framework to harness the power of artificial intelligence

With a robust AI infrastructure, companies have the foundational elements they need to optimally benefit from the power of artificial intelligence. By investing in dedicated infrastructure, businesses position themselves to use AI to good effect across the board, helping put them on the road to transformative outcomes.

If you’re eager to bring AI infrastructure to your business, consider that specialized providers can offer tailored solutions to suit different industries, and there’s likely to be a solution out there that’s right for you. On the other hand, a custom on-premises deployment can bring enhanced AI security and data governance if you need strict compliance.

Whatever your final choice, businesses that invest extensively in AI are likely to lead the way in everything from data analysis to innovation, staying ahead of those who choose not to.

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AI infrastructure: What enterprises need to know