RED HAT AI PLATFORM

The essence of the Red Hat AI platform

The Red Hat AI platform is an open source, enterprise-grade artificial intelligence solution that helps manage the entire lifecycle of AI models, from development to production. Its open technology approach reduces vendor lock-in while providing enterprise-grade security and reliability. 

Components of the Red Hat AI platform

Red Hat OpenShift AI

The central element of the platform is a Kubernetes-based MLOps environment. It supports data preparation, model development, training, testing, version management, and model deployment. It offers built-in tools for data engineers (e.g., notebooks, pipelines) and operators..

Red Hat Enterprise Linux AI (RHEL AI)

RHEL AI is an AI-optimized Linux distribution that includes pre-integrated, validated open source LLMs and tools. It is primarily designed for generative AI and fine-tuning base models to serve enterprise needs.

Red Hat AI Inference Server

An enterprise-grade, scalable runtime environment for fast and secure deployment of artificial intelligence models, especially generative AI and LLM models. It provides optimized inference on CPUs and GPUs while integrating natively with the Red Hat OpenShift environment. 

Typical OpenShift AI use cases

 The Red Hat AI platform supports a wide range of enterprise AI use cases thanks to its flexible architecture. The following use cases are the most common and illustrate how the platform can create business value.

Generative AI and corporate assistants

Red Hat AI is the ideal foundation for creating enterprise chatbots, internal knowledge assistants, and generative AI applications. Organizations can run language models fine-tuned to their own data in a secure, controlled environment, either on-premises or in a hybrid cloud. This enables the introduction of assistants that support customer service automation, internal IT support, or business analytics.

AI-based application development

The platform supports the direct integration of AI functions into modern, microservice-based applications. Developers and data scientists can work together in a shared environment, while AI models are deployed in a containerized, scalable manner. This accelerates innovation and reduces the gap between development and operations.

Predictive analytics and business decision support

Red Hat AI is suitable for teaching and running classic machine learning models, such as forecasting, risk analysis, or demand forecasting. The models can be integrated into existing business systems, enabling real-time support for data-driven decision-making.

MLOps and model lifecycle management

One of the platform's key areas is MLOps: version management, automated training, testing, and monitoring of AI models. Red Hat AI helps standardize and make AI processes transparent, so models can be operated reliably and audibly in a corporate environment.

Edge AI and industrial applications

Red Hat AI supports the execution of AI models in edge environments, such as manufacturing, logistics, or IoT solutions. Centrally managed but locally executed models ensure fast response times and high availability, even in limited network environments.

Safe, regulated AI implementation

The platform is particularly well suited to industries (finance, healthcare, public administration) where data protection and compliance are of paramount importance. Red Hat AI enables AI solutions to be deployed in compliance with corporate security and compliance policies.

Skills, advantages

End-to-end AI and MLOps support
Red Hat AI covers the entire lifecycle of AI models, from data preparation and development through training, testing, and version management to production deployment. Its built-in MLOps capabilities help automate, standardize, and scale processes. Managing a Kubernetes environment can be complicated and cumbersome for those without hands-on experience in deployment and usage, and it becomes even more complex when there are a large number of application instances. Kubernetes operators can simplify the management of stateful applications that use other components such as databases, caches, and monitoring environments to function. They create certified application packages using standard Kubernetes tools in line with best practices, while automating tasks such as updating, backing up, and scaling. Red Hat OpenShift supports the use of Kubernetes Operators, using them as a model for scaling applications while reducing operational tasks and ensuring operational consistency.
Open source and vendor-independent approach
The platform is built on open technologies, avoiding vendor lock-in. Organizations are free to choose models, frameworks, and infrastructure while maintaining enterprise-level support and stability.
Hybrid and multicloud operation
Red Hat AI can be used in a unified manner on-premises, in private and public clouds, and in edge environments. This allows for flexible placement of AI workloads based on business, data protection, or cost considerations.
Enterprise-level security and compliance
Built-in security mechanisms—access control, isolation, auditability, and logging—ensure that AI solutions comply with strict corporate and industry regulations.
Scalable, optimized inference
Red Hat AI supports efficient model execution on CPUs, GPUs, and AI accelerators. Inference is scalable, monitorable, and cost-effective, even for high-load generative AI applications. With Red Hat OpenShift Serverless, applications can easily use only the resources they actually need, as resources are automatically scaled up or down based on current load and demand. This approach removes the burden of deploying and configuring servers from developers, allowing them to focus on application development instead. OpenShift Serverless helps developers deploy and run serverless applications that can scale up and down flexibly, with zero load and resource usage. With Red Hat OpenShift Pipeline, developers can take complete control of their application deployment processes, plug-ins, and access management without the need for a central CI/CD server. OpenShift Pipeline runs each step of the CI/CD pipeline in its own container, enabling each step to be scaled independently to meet pipeline requirements. It provides a streamlined, task-oriented user experience through the OpenShift console developer view, command line interface (CLI), and integrated development environment (IDE).
Tight integration with the OpenShift ecosystem
The platform is built natively on Red Hat OpenShift, leveraging best practices in containerization, Kubernetes, and DevOps. This simplifies operations and the integration of AI into existing application environments. Red Hat OpenShift is fully capable of meeting the needs of IT organizations and application developers. Customers can choose from a wide range of Kubernetes-based solutions, from platforms built on their own or community projects to managed services built on public clouds or their own infrastructure. As one of the market's leading solutions, Red Hat OpenShift offers a complete solution for customers who do not want to build and maintain their own custom platform based on the upstream project, acquire the necessary deep expertise, but need a secure, supported Kubernetes platform for their day-to-day business operations.
Effective cooperation between teams
Red Hat AI provides a common platform for data scientists, developers, and operators. A unified set of tools and processes reduces handoffs and accelerates the business adoption of AI solutions.

Recommended courses

  • Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)