AI infrastructure explained

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With artificial intelligence (AI) growing in use with our daily lives, it’s crucial to have a structure that allows effective and efficient workflows. That’s where artificial intelligence infrastructure (AI infrastructure) comes in. 

A well-designed infrastructure helps data scientists and developers access data, deploy machine learning algorithms, and manage the hardware’s computing resources.

AI infrastructure combines artificial intelligence and machine learning (AI/ML) technology to develop and deploy reliable and scalable data solutions. It is the technology that enables machine learning, allowing machines to think like humans.

Machine learning is the technique of training a computer to find patterns, make predictions, and learn from experience without being explicitly programmed. It can be applied to generative AI, and is made possible through deep learning, a machine learning technique for analyzing and interpreting large amounts of data.

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AI infrastructure tech stack 

A tech stack, short for technology stack,  is a set of technologies, frameworks, and tools used to build and deploy software applications. As a visual, these technologies “stack” on top of each other to build an application. An AI infrastructure tech stack can enable faster development and deployment of applications through three essential layers. 

The applications layer allows humans and machines to collaborate with essential workflow tools, including end-to-end apps using specific models or end-user-facing apps that aren’t specific. End-user-facing applications are usually built using open-source AI frameworks to create models that are customizable and can be tailored to meet specific business needs. 

The model layer consists of checkpoints that power AI products. This layer requires a hosting solution for deployment. There are three models to this layer that provide a foundation.

  • General AI: the artificial intelligence that replicates human-like thinking and decision-making processes. Think of AI apps like ChatGPT and DALL-E from OpenAI.
  • Specific AI: the artificial intelligence that is trained on very specific and relevant data to perform with greater precision. Think of tasks like generating ad copy and song lyrics. 
  • Hyperlocal AI: the artificial intelligence that can achieve the highest levels of accuracy and relevance, designed to be specialists in their field. Think of writing scientific articles or creating interior design mockups

The infrastructure layer consists of hardware and software components that are necessary for building and training AI models. Components like specialized processors like GPUs (hardware) and optimization and deployment tools (software) fall under this layer. Cloud computing services are also a part of the infrastructure layer. 

Now that we have covered the three layers involved in an AI infrastructure, let’s explore a few components that are required to build, deploy, and maintain AI models. 

Data storage

Data storage is the collection and retention of digital information—the bits and bytes behind applications, network protocols, documents, media, address books, user preferences, and more. A strong data storage and management system is important for storing, organizing, and retrieving the amount of data needed in AI training and validation.

Data management

Data management is the process of gathering, storing, and using data, often facilitated by data management software. It allows you to know what data you have, where it is located, who owns it, who can see it, and how it is accessed. With the appropriate controls and implementation, data management workflows deliver the analytical insights needed to make better decisions.

Machine learning frameworks

Machine learning (ML) is a subcategory of artificial intelligence (AI) that uses algorithms to identify patterns and make predictions within a set of data. Machine learning frameworks provide tools and libraries for designing, training, and validating machine learning models. 

Machine learning operations 

Machine learning operations (MLOps) is a set of workflow practices that aims to streamline the process of producing, maintaining, and monitoring machine learning (ML) models. Inspired by DevOps and GitOps principles, MLOps seeks to establish a continuous and ever-evolving process for integrating ML models into software development processes.  

A well-designed AI infrastructure makes way for successful AI and machine learning (ML) operations. It drives innovation and efficiency. 


AI infrastructure has several benefits for your AI operations and organizations. One benefit is scalability, providing the opportunity to upscale and downscale operations on demand, especially with cloud-based AI/ML solutions. Another benefit is automation, allowing repetitive work to decrease errors and increase deliverable turn around times. 


Despite its benefits, AI infrastructure does have some challenges. One of the biggest challenges is the amount and quality of data that needs to be processed. Because AI systems rely on large amounts of data to learn and make decisions, traditional data storage and processing methods may not be enough to handle the scale and complexity of AI workloads. Another big challenge is the requirement for real-time analysis and decision-making. This requirement means that the infrastructure has to process data quickly and efficiently, which needs to be taken into account to integrate the right solution to deal with large volumes of data.


There are applications that can address these challenges. With Red Hat® OpenShift® cloud services, you can build, deploy, and scale applications quickly. You can also enhance efficiency by improving consistency and security with proactive management and support. Red Hat Edge helps you deploy closer to where data is collected and gain actionable insights.

AI is not only impacting our daily lives, but our organizations as well. Powering new discoveries and experiences across fields and industries, Red Hat’s open source platforms can help you build, deploy, and monitor AI models and applications, and take control of your future.

Red Hat OpenShift AI provides a flexible environment for data scientists, engineers and developers to build, deploy, and integrate projects faster and more efficiently, with benefits including built-in security and operator life cycle integration. It provides Jupyter-as-a-service, with associated TensorFlow, Pytorch and other framework libraries. Plus, several software technology partners (Starburst, IBM, Anaconda, Intel and NVIDIA) have been integrated into the AI service, making it easier to discover and try new tooling—from data acquisition to model building to model deployment and monitoring—all in a modern cloud-native environment.

Our AI partners build on the Red Hat infrastructure to complete and optimize AI/ML application development. They help complete the AI lifecycle with solutions ranging from data integration and preparation, to AI model development and training, to model serving and inferencing (making predictions) based on new data. 



InstructLab is an open source project for enhancing large language models (LLMs).

Keep reading


What is generative AI?

Generative AI relies on deep learning models trained on large data sets to create new content.


What is machine learning?

Machine learning is the technique of training a computer to find patterns, make predictions, and learn from experience without being explicitly programmed.


What are foundation models?

A foundation model is a type of machine learning (ML) model that is pre-trained to perform a range of tasks. 

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