What is AI infrastructure?
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Published: 8 April 2024
Contributors: Mesh Flinders, Ian Smalley

What is AI infrastructure?

AI (artificial intelligence) infrastructure, also known as an AI stack, is a term that refers to the hardware and software needed to create and deploy AI-powered applications and solutions. 

Strong AI infrastructure enables developers to effectively create and deploy AI and machine learning (ML) applications like chatbots such as OpenAI’s Chat GPT, facial and speech recognition, and computer vision. Enterprises of all different sizes and across a wide range of industries depend on AI infrastructure to help them realize their AI ambitions. Before we get into what makes AI infrastructure important and how it works, let’s look at some key terms.

What is artificial intelligence?

AI is technology that allows computers to simulate the way humans think and solve problems. When combined with other technologies—such as the internet, sensors, robotics and more—AI technology can perform tasks that typically require human input, such as operating a vehicle, responding to questions or delivering insights from large volumes of data. Many of AI’s most popular applications rely on machine learning models, an area of AI that focuses specifically on data and algorithms.

 

What is machine learning (ML)?

ML is a focus area of AI that uses data and algorithms to imitate the way humans learn, improving the accuracy of its answers over time. ML relies on a decision process to make a prediction or classify information, an error function that evaluates the accuracy of its work, and a large language model (LLM) and model optimization process that reduces discrepancies between known examples and model estimates. An ML algorithm repeats this “evaluate and optimize” process until a defined threshold accuracy for the model has been met.

To learn more about the nuanced differences between AI and ML, check out our blog, “AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?” 

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AI infrastructure vs. IT infrastructure

As enterprises discover more and more ways to use AI, creating the necessary infrastructure to support its development has become paramount. Whether deploying ML to spur innovation in the supply chain or preparing to release a generative AI chatbot, having the right infrastructure in place is crucial. 

The primary reason AI projects require bespoke infrastructure is the sheer amount of power needed to run AI workloads. To achieve this kind of power, AI infrastructure depends on the low-latency of cloud environments and the processing power of graphics processing units (GPUs) rather than the more traditional central processing units (CPUs) typical of traditional IT infrastructure environments.  

Additionally, AI infrastructure concentrates on hardware and software specially designed for the cloud and AI and ML tasks rather than the PCs, software and on-premise data centers that IT infrastructure favors. In an AI ecosystem, software stacks usually include ML libraries and frameworks like TensorFlow and PyTorch, programming languages like Python and Java, and distributed computing platforms like Apache Spark or Hadoop.

The benefits of AI infrastructure

In addition to supporting the development of cutting-edge applications for customers, enterprises investing in AI infrastructure typically see big improvements to their processes and workflow. Here are six of the most common benefits that businesses who develop strong AI infrastructure can expect:

Increased scalability and flexibility

Since AI infrastructure is typically cloud-based, it’s much more scalable and flexible than its on-premises IT predecessors. As the datasets needed to power AI applications become larger and more complex, AI infrastructure is designed to scale with them, empowering organizations to increase the resources on an as-needed basis. Flexible cloud infrastructure is highly adaptable and can be scaled up or down easily than more traditional IT infrastructure as an enterprise’s requirements change.

Greater performance and speed

AI infrastructure utilizes the latest high-performance computing (HPC) technologies available, such as GPUs and tensor protocol units (TPUs), to power the ML algorithms that underpin AI capabilities. AI ecosystems have parallel processing capabilities significantly reducing the time needed to train ML models. Since speed is crucial in many AI applications, such as high-frequency trading apps and driverless cars, the improvements in speed and performance are a critical feature of AI infrastructure.

More collaboration

Strong AI infrastructure isn’t just about hardware and software, it also provides developers and engineers with the systems and processes they need to work together more effectively when building AI apps. Relying on MLOps practices, a lifecycle for AI development built to streamline and automate ML model creation, AI systems enable engineers to build, share and manage their AI projects more effectively.

Better compliance

As concerns around data privacy and AI have increased, the regulatory environment has become more complex. As a result, robust AI infrastructure must ensure privacy laws are observed strictly during data management and data processing in the development of new AI applications. AI infrastructure solutions ensure all applicable laws and standards are closely followed and that AI compliance is enforced, protecting user data and keeping enterprises safe from legal and reputational damage.

Reduced costs

While investing in AI infrastructure can be expensive, the costs associated with trying to develop AI applications and capabilities on traditional IT infrastructure can be even more costly. AI infrastructure ensures the optimization of resources and the utilization of the best available technology in the development and deployment of AI projects. Investing in strong AI infrastructure provides better return on investment (ROI) on AI initiatives than trying to accomplish them on outdated, inefficient IT infrastructure.

Exploitation of generative AI capabilities

Generative AI, also called Gen AI, is AI that can create its own content, including text, images, video and computer code, using simple prompts from users. Since the launch of ChatGPT, a generative AI application, two years ago, enterprises around the globe have been eagerly trying out new ways to leverage this new technology. Generative AI can increase productivity for both enterprises and individuals exponentially. But it comes with real risks. AI infrastructure with a strong framework around generative AI can help businesses develop its capabilities safely and responsibly.  

How does AI infrastructure work?

To give engineers and developers the resources they need to build advanced AI and ML applications, AI infrastructure relies on a blend of modern hardware and software. Typically, AI infrastructure is broken down into four components: Data storage and processing, compute resources, ML frameworks and MLOps platforms. Here’s a more detailed look at how they function.

Data storage and processing

AI applications need to train on large datasets to be effective. Enterprises looking to deploy strong AI products and services need to invest in scalable data storage and management solutions, such as on-premises or cloud-based databases, data warehouses and distributed file systems. Additionally, data processing frameworks and data processing libraries like Pandas, SciPy and NumPy are often needed to process and clean data before it can be used to train an AI model. 

Compute resources

ML and AI tasks require large amounts of compute power and resources to run. Well-designed AI infrastructure often includes specialized hardware like a graphics processing unit (GPU) and a tensor processing unit (TPU) to provide parallel processing capabilities and speed ML tasks.

Graphics processing units (GPUs): GPUs, typically made by Nvidia or Intel, are electronic circuits used to train and run AI models because of their unique ability to perform many operations at once. Typically, AI infrastructure includes GPU servers to speed matrix and vector computations that are common in AI tasks.

Tensor processing Units (TPUs): TPUs are accelerators that have been custom built to speed tensor computations in AI workloads. Their high throughput and low latency make them ideal for many AI and deep learning applications.

Machine learning frameworks

ML frameworks provide specific resources that AI needs to design, train and deploy ML models. ML Frameworks like TensorFlow and PyTorch support a variety of capabilities required by AI applications, including the speeding of GPU tasks and functionality critical to the three types of ML training: supervised, unsupervised and reinforcement training. Strong ML frameworks speed the process of machine learning and give developers the tools they need to develop and deploy AI applications.

MLOps platforms

MLOps is a process that involves a set of specific practices to help automate and speed machine learning. MLOps platforms aid developers and engineers in data collection and model training, all the way through validation, troubleshooting and monitoring an application once it has been launched. MLOps platforms underpin AI infrastructure functionality, helping data scientists, engineers and others successfully launch new AI-capable tools, products and services. 

Six steps to building strong AI infrastructure

Here are six steps enterprises of all sizes and industries can take to build the AI infrastructure they need:

1. Define your budget and objective

Before you investigate the many options available to businesses wanting to build and maintain an effective AI infrastructure, it’s important to clearly set down what it is you need from it. Which problems do you want to solve? How much are you willing to invest? Having clear answers to questions like these is a good place to start and will help streamline your decision-making process when it comes to choosing tools and resources.

2. Choose the right hardware and software

Selecting the right tools and solutions to fit your needs is an important step towards creating AI infrastructureyou can rely on. From GPUs and TPUs to speed machine learning, to data libraries and ML frameworks that make up your software stack, you’ll face many important choices when selecting resources. Always keep in mind your goals and the level of investment you’re willing to make and assess your options accordingly.

3. Find the right networking solution

The fast, reliable flow of data is critical to the functionality of AI infrastructure. High-bandwidth, low-latency networks, like 5G, enable the swift and safe movement of massive amounts of data between storage and processing. Additionally, 5G networks offer both public and private network instances for added layers of privacy, security and customizability. The best AI infrastructure tools in the world are useless without the right network to allow them to function the way they were designed.

4. Decide between cloud and on-premises solutions

All the components of AI infrastructure are offered in both the cloud as well as on-premises, so it’s important to consider the advantages of both before deciding which is right for you. While cloud providers like AWS, Oracle, IBM and Microsoft Azure offer more flexibility and scalability, allowing enterprises access to cheaper, pay-as-you go models for some capabilities, on-premise AI infrastructure has its advantages too, often providing more control and increasing the performance of specific workloads.

5. Establish compliance measures

AI and ML are highly regulated areas of innovation and as more and more companies launch applications in the space, it is only becoming even more closely watched. Most of the current regulations governing the sector are around data privacy and security and can cause businesses to incur damaging fines and reputational damage if they’re violated.

6. Implement and maintain your solution

The last step in building your AI infrastructure is launching and maintaining it. Along with your team of developers and engineers who will be utilizing it, you’ll need ways to ensure the hardware and software is kept up to date and the processes you’ve put in place are followed. This typically includes the regular updating of software and running of diagnostics on systems, as well as the review and auditing of processes and workflows.

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