How to Modernize Your Enterprise Platform for Production AI with Azure Red Hat OpenShift
Introduction
As artificial intelligence moves from experimental pilots to full-scale production, the real challenge shifts from building models to operating them reliably, securely, and at scale. At Red Hat Summit 2026, Microsoft and Red Hat showcased how Microsoft Azure Red Hat OpenShift provides a unified, enterprise-grade platform that supports this critical transition. The recognition of Microsoft as the Platform Modernization Partner of the Year—alongside the North American Hybrid Cloud Everywhere honorable mention—underscores the impact of this collaboration. A standout example is Banco Bradesco, one of Latin America’s largest financial institutions, which moved beyond AI experimentation to production by unifying governance across over 200 AI initiatives on Azure Red Hat OpenShift. This guide walks you through the same proven approach to modernize your platform and deploy production AI with consistent identity, governance, and security.
What You Need
Before you start, ensure you have the following prerequisites and resources in place:
- An active Azure subscription with permissions to create resources
- Familiarity with Red Hat OpenShift and container orchestration
- Knowledge of Azure Active Directory (Azure AD) for identity management
- Understanding of containerization (Docker, Kubernetes) for AI model packaging
- Access to Azure Policy and Azure Security Center for governance
- A sample AI model or pilot project to deploy (e.g., a machine learning inference service)
- Optional: A team with roles for platform operations, security, and data science
Step 1: Assess Your Current Environment and AI Readiness
Review your existing infrastructure, current AI projects, and operational constraints. Identify any pilot AI workloads that are ready to move to production. Map out security, compliance, and governance requirements (for example, Banco Bradesco’s strict financial regulations). This assessment will guide your architecture decisions for integration with Azure services.
Step 2: Provision an Azure Red Hat OpenShift Cluster
Deploy a managed OpenShift cluster through the Azure portal or CLI. Choose a region and node size based on your AI workload demands (CPU/GPU). Enable high availability and configure networking to integrate with your virtual network. Use the Azure Red Hat OpenShift resource type, which provides a jointly supported, enterprise-ready Kubernetes platform.
Step 3: Integrate Azure Identity and Security Services
Connect your OpenShift cluster to Azure Active Directory for single sign-on and role-based access control. Apply Azure Policy to enforce compliance rules across the cluster—such as restricting privileged containers or ensuring encryption. Enable Azure Defender for container security to monitor threats. This integration ensures consistent governance, as demonstrated by Banco Bradesco’s unified approach across 200+ AI initiatives.
Step 4: Containerize and Deploy Your AI Models
Package your AI models into containers using Docker. Create Kubernetes deployment manifests with resource limits, health probes, and scaling policies. Deploy these containers to your Azure Red Hat OpenShift cluster using `oc` or the web console. Use Azure Container Registry to store your images securely and Azure Key Vault to manage secrets (e.g., API keys, model credentials).
Step 5: Establish Governance Across Multiple AI Initiatives
Leverage OpenShift’s Project and Namespace capabilities to isolate different AI workloads. Use Azure Policy and OpenShift OPA Gatekeeper to enforce uniform policies—such as required labels, resource quotas, or allowed registries. Set up logging and monitoring with Azure Monitor and OpenShift Logging to track performance and compliance. This centralized governance mirrors how Banco Bradesco manages its large portfolio of AI projects.
Step 6: Scale from Pilot to Production
Move from a single namespace pilot to a full production rollout. Configure auto-scaling for your AI workloads using Horizontal Pod Autoscaler based on CPU/memory or custom metrics (e.g., request latency). Integrate with Azure Load Testing to validate performance under load. Implement a CI/CD pipeline with Azure DevOps or GitHub Actions to automate deployments. Ensure your cluster meets service-level agreements (SLAs) by leveraging the joint support from Microsoft and Red Hat.
Step 7: Monitor, Optimize, and Iterate
Continuously monitor your production AI workloads using Azure Monitor and OpenShift Monitoring Stack. Use Azure Cost Management to track spending and optimize resource usage. Set up alerts for anomalies or performance degradation. Regularly review governance policies and update them as your AI portfolio grows. Incorporate feedback from operations and data science teams to refine the platform. This iterative approach ensures long-term success.
Tips for Success
- Start with a single, non-critical AI workload to validate the integration and governance setup before scaling.
- Leverage joint support from Microsoft and Red Hat – you get a single point of contact for any platform issues.
- Use managed Azure services (e.g., Azure Database, Azure Cache for Redis) to offload operational overhead from your AI stack.
- Document policies and procedures early so that every AI team can onboard consistently.
- Plan for networking – ensure your cluster can securely access on-premises or other Azure resources via ExpressRoute or VPN.
- Engage your security team early to define compliance boundaries within OpenShift and Azure.
By following these steps, you can replicate the success of industry leaders like Banco Bradesco, turning your AI experiments into reliable, governed, and scalable production systems on Azure Red Hat OpenShift.