Given that AI infrastructure refers to the various hardware and software components that support AI solutions, people sometimes use the term synonymously with 'IT infrastructure'. But the truth is that AI infrastructure and IT infrastructure are designed with distinct requirements, and each serves unique purposes.

AI infrastructure is specifically built to support AI and machine learning workloads, relying heavily on high-performance computing resources. In contrast, traditional IT infrastructure is designed for more general computing tasks, supporting broader IT operations with more generic hardware and software. 

In other words, IT infrastructure supports day-to-day business operations and general IT services, while AI infrastructure is optimised for developing, deploying and scaling AI solutions. This enables businesses to harness the power of AI to achieve a competitive advantage.  

AI infrastructure is composed of multiple layers designed to work together to support AI models. These layers include the applications layer, the model layer and the infrastructure layer: 

• Applications layer
  This layer encompasses the AI-powered applications and solutions that end-users interact with, such as AI chatbots, recommendation systems and predictive analytics tools.
• Model layer
  This layer involves the creation and training of machine learning models that power the AI applications. It includes the algorithms and processes required to develop these models.
• Infrastructure layer
  The foundation of AI, this layer provides the essential hardware and software components necessary to support the model and applications layers.

The infrastructure layer is critical, as it enables efficient processing, storage and management of data, along with the computational power needed for training and deploying AI models. The key components of AI infrastructure can typically be categorised as either 'hardware' or 'software'.

Hardware refers to the physical devices and equipment that provide the computational power and storage capacity necessary for AI operations. These include:

• GPU servers 
  Graphics processing units (GPUs) are essential for AI tasks due to their ability to perform parallel processing, making them ideal for training machine learning models. GPU servers provide the computational power necessary for handling large datasets and complex calculations efficiently.
• AI accelerators 
  AI accelerators are specialised hardware designed to optimise the performance of AI applications. They include custom chips and co-processors that enhance the speed and efficiency of machine learning tasks, reducing the time required for training and inference.
• TPUs 
  Tensor processing units (TPUs) are specialised processors developed specifically for accelerating machine learning workloads. They are optimised for tensor computations—a common operation in neural networks—and significantly speed up the training and deployment of deep learning models.

Software relates to the digital programs, applications and frameworks that operate within AI systems. Key software components include:

• Data storage 
  Data storage is crucial for retaining the vast amounts of digital information required for training and validating AI models. Reliable data storage systems (such as databases, data warehouses or data lakes) help keep data organised, secure and easy to retrieve.
• Data processing libraries 
  Data processing libraries are essential for preparing data for AI applications. They enable the cleaning, transformation and structuring of large datasets, allowing for distributed processing to speed up these tasks. Efficient data processing is vital for training accurate and reliable AI models.
• Data management 
  Data management involves the processes of collecting, storing and using data effectively. It ensures that data is accessible and compliant with privacy regulations. Proper data management supports the analytical insights needed for informed decision-making in AI projects.
• Machine learning framework 
  Machine learning frameworks provide the necessary tools and libraries for designing, training and validating machine learning models. They support various functionalities, such as automatic differentiation, optimisation and neural network layers, often with GPU acceleration for faster computations.
• MLOps platforms 
  Machine learning operations (MLOps) streamlines the machine learning lifecycle by automating and managing processes, from data collection and model training to deployment and monitoring. These platforms facilitate version control, automated training and deployment pipelines and model performance tracking, enhancing collaboration between data scientists and ML engineers

AI infrastructure operates through the integration of these components, working in unison to support AI and ML applications.  

Data storage and processing frameworks manage and prepare large datasets, ensuring they are clean and structured. Computing resources, including GPUs and TPUs, provide the necessary computational power for training and running AI models, while machine learning frameworks facilitate the design and deployment of these models. Through it all, MLOps platforms automate and optimise the entire lifecycle. Employed correctly, this kind of cohesive system ensures efficient, scalable and effective AI operations. 

AI infrastructure is crucial for making AI work smoothly and efficiently. It provides easy access to data, helping data scientists and developers quickly build and deploy AI models. This setup simplifies tasks that could otherwise present challenges (such as data cleaning and model training), reducing time and effort and accelerating innovation. 

Another important aspect of AI infrastructure is its ability to process data in real time, which is essential for tasks like image recognition and language translation. Specialised hardware and software work together to handle large data volumes and complex calculations, ensuring faster and more accurate results. AI infrastructure is likewise designed to grow with the needs of the organisation, making it a reliable investment for evolving businesses. 

The concept of an 'AI factory' takes this further by creating a unified system for the entire AI development process. This approach automates and scales AI projects, allowing for continuous innovation across various industries. By using an AI factory, businesses can stay competitive while fully utilising AI technologies to address and adapt to changing objectives. 

When designing AI infrastructure, several key factors should be addressed to ensure it meets the organisation's needs. Before committing to any specific approach, consider the following elements of successful AI infrastructures:

• Efficient workflows 
  AI infrastructure should facilitate smooth workflows for data ingestion, preprocessing, model training, validation and deployment. Efficient AI workflows reduce time-to-insight and enhance productivity, ensuring that AI models are trained accurately and quickly.
• Adequate storage 
  Sufficient storage systems are necessary for managing the massive stores of data required for AI applications. Managed efficiently, storage solutions keep computational resources continuously active, maximising utilisation and reducing overall costs.
• Adaptability and scalability 
  AI infrastructure must be scalable and flexible to accommodate growing datasets and evolving AI models. Cloud-based solutions offer scalability, allowing organisations to expand or reduce resources as needed, supporting varying workloads efficiently.
• Effective security and compliance 
  Security and compliance are paramount for protecting sensitive data. AI infrastructure should include comprehensive security measures and an integrated governance, risk and compliance (GRC) strategy, maintaining data privacy and ensuring adherence to established laws, policies and regulations.
• Ease of integration 
  Seamless integration with existing IT systems makes it possible to leverage existing data and infrastructure to help support AI applications. Successful integration aligns AI initiatives with overall IT strategy, ensuring consistency and efficiency across the company.
• Future-proofing 
  AI infrastructure should be more than a short-term solution; it must be adaptable to future advancements. Investing in modular, upgradable systems and staying informed about emerging AI trends helps organisations maintain a cutting-edge infrastructure that evolves with technological advancements. 

With the right considerations addressed, organisations can now move forward with designing and deploying the AI infrastructure. This involves strategic planning and execution to ensure that the solutions meet the needs of the business. The following steps are principal elements in this process:

• Identify objectives 
  Start by defining clear objectives for what the AI infrastructure is meant to achieve. Determine the problems it will solve and the specific outcomes that are expected from it. This clarity will guide other decisions regarding the tools and resources.
• Establish a budget 
  Set a realistic budget that aligns with the AI objectives. Consider the costs of hardware, software, cloud services and maintenance. A well-defined budget helps prioritise investments and ensures that resources are allocated efficiently.
• Select the right hardware and software 
  Choose the appropriate hardware and software that match the organisation's AI needs. This includes GPUs, TPUs, data storage solutions, machine learning frameworks and MLOps platforms. Ensure that the selected components are compatible and capable of handling AI workloads effectively.
• Identify an effective networking solution 
  Reliable and fast data transfer is a prerequisite for most AI operations. Invest in high-bandwidth, low-latency networking solutions to support the seamless flow of data between storage and processing units. Consider technologies like 5G for enhanced performance and security.
• Weigh different computing options 
  Decide whether to deploy the AI infrastructure on the cloud or on-premises. Cloud solutions offer scalability and flexibility with pay-as-you-go models, while on-premises solutions may provide more control and better performance for specific workloads.
• Integrate compliance measures 
  Implement proven compliance measures to adhere to data privacy regulations and industry standards. Ensure that the AI infrastructure includes security protocols and governance frameworks to protect sensitive data and maintain regulatory compliance.
• Deploy the infrastructure 
  Execute the deployment plan for the AI infrastructure, ensuring that all components are properly integrated and configured. This phase involves setting up hardware, installing software and establishing networking connections.
• Track, maintain and improve the infrastructure over time 
  Regularly monitor the performance of the AI infrastructure. Conduct maintenance to address any issues and optimise performance. Continuously evaluate and improve the infrastructure to keep up with technological advancements and evolving business needs.

Establishing an effective AI infrastructure empowers businesses to leverage AI's full potential. Unfortunately, it can also be a complex and challenging task. ServiceNow simplifies this process by offering powerful solutions through the award-winning Now Platform®. 

The Now Platform leads the industry in advanced, built-in AI capabilities, allowing businesses to integrate and utilise artificial intelligence without the need to build an infrastructure from scratch. ServiceNow applications feature powerful AI functionalities for streamlining workflows, automating complex tasks and enhancing productivity — all fully integrated and centralised, for comprehensive visibility and total control.  

Experience the benefits of a solid AI infrastructure to support your business goals. Schedule a demo today and get the foundation you need to reach the sky.