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Artificial intelligence is no longer just a trend, but an integral part of modern corporate strategies. At the same time, the requirements for security, scalability and compliance are increasing, especially in regulated industries such as banking, insurance and public administration.
With OpenShift AI, Red Hat offers a platform that is specifically designed to operate AI applications in a production-ready, data protection-compliant and flexible manner. Companies can run their models wherever their data is, whether on-premises, in the private cloud or in hybrid architectures, and thus retain full control at all times.
What is OpenShift AI?
OpenShift AI is an extension of Red Hat OpenShift that natively integrates Machine Learning (ML) and Generative AI into a Kubernetes-based infrastructure. It enables data scientists, ML engineers and developers to work together on AI solutions from the experimentation phase to training and deployment in a productive environment.
Key features:
- Integrated MLOps pipelines for continuous training, testing and deployment
- GPU-optimized containers for compute-intensive AI workloads
- Role & rights concepts for secure multi-tenancy
- Open architecture with support for open source and commercial models
- Flexible scaling horizontally (more instances) and vertically (more resources)
- Monitoring & governance through integrated tools
Conclusion: OpenShift AI is not a stand-alone solution, but a complete platform that combines the development, security and operation of AI and meets the high requirements of companies.
Why OpenShift AI is crucial for companies
1. data sovereignty
The platform can be operated entirely within the company’s own infrastructure. Sensitive data never leaves the company network – a must in regulated industries.
2. scalability & performance
Resources such as GPUs are only allocated when they are needed. This shortens training times and optimizes operating costs.
3. production-ready
OpenShift AI is not only intended for proof-of-concepts – it supports the entire lifecycle of AI applications, including versioning, CI/CD integration, logging and security policies.
Practical use cases for OpenShift AI
The strength of OpenShift AI lies in the fact that very different AI workloads can be operated securely and scalably. Here are three practical examples:
1. secure translation service – data protection compliant translations
Challenge:
Companies need to translate texts, emails or documents into other languages without transferring sensitive data such as IBANs, customer numbers or addresses to external services.
Solution with OpenShift AI:
- Pre-processing: masking sensitive data using regex and Named Entity Recognition (NER) (e.g. spaCy)
- Translation: Use of open source models such as MarianMT or M2M-100 directly on OpenShift AI; optional connection to external APIs (only with customer consent)
- Post-processing: Reverse engineering of the original data into the translated text
- UI: Web front end (React/Vue) for easy operation
Advantages:
- No text leaves the OpenShift environment
- Auditable processing
- Can be used in highly sensitive environments (banks, insurance companies, authorities)
2. microservice documentation AI – understanding architecture at a glance
Challenge:
In complex microservice landscapes, it is difficult to keep an eye on dependencies, interfaces and potential risks in the event of changes.
Solution with OpenShift AI:
- Code & API crawler: Analyzes repositories, OpenAPI specifications and service documentation
- Automatic mapping: recognizes dependencies and data flows between services
- LLM support: Answers questions such as “Which systems will be affected if I change service X?”
- Visualization: Representation of the architecture in interactive diagrams (Mermaid)
Advantages:
- Quick overview for DevOps and architecture teams
- Less risk with deployments
- Fully usable internally, no dependency on third-party providers
3. e-mail classification & routing – automation in the inbox
Challenge:
Support and compliance departments spend hours every day manually categorizing and forwarding incoming emails.
Solution with OpenShift AI:
- Preprocessing: Anonymization of personal data before analysis
- Fine-tuned classifier: Trained on company-specific categories (“inquiry”, “complaint”, “termination”)
- Automatic routing: forwarding to responsible departments via API
- Feedback loop: Human-in-the-loop approach to continuous improvement
Advantages:
- GDPR-compliant
- Significantly reduced processing times
- Adaptable to internal processes
Possible technology stack for implementation
| Level | Technology / Tool |
|---|---|
| Platform | OpenShift AI, Kubernetes |
| Preprocessing | spaCy, Microsoft Presidio, Regex pipelines |
| LLM / Models | HuggingFace (MarianMT, BERT), Mistral, LLaMA, GPT |
| Visualization | Mermaid |
| UI | React |
Conclusion
OpenShift AI provides companies with an end-to-end platform for the secure operation of AI applications, from development and training to productive use.
The combination of security, scalability and openness makes OpenShift AI a strategic enabler for industries in which data protection and compliance have top priority. At the same time, the platform creates the basis for implementing innovations quickly and sustainably.
Source:
Red Hat OpenShift AI
