How to Modernize Your Platform and Run Production AI on Azure Red Hat OpenShift

Introduction

In a world where artificial intelligence is moving rapidly from proof-of-concept to production, organizations face the critical challenge of operating AI workloads with consistent governance, security, and scalability. At Red Hat Summit 2026, Microsoft and Red Hat showcased how Microsoft Azure Red Hat OpenShift (ARO) provides a jointly supported, enterprise-ready platform that addresses exactly this challenge. The recognition of Microsoft as the Platform Modernization Partner of the Year in the Red Hat Ecosystem Innovation Awards—and the Honorable Mention for North American Hybrid Cloud Everywhere—underscores the real-world impact of this collaboration. A standout example is Banco Bradesco, a major Latin American financial institution that moved beyond AI experimentation to production by building on ARO, unifying governance across more than 200 AI initiatives. This guide walks you through the steps to achieve similar platform modernization and production AI deployment using Azure Red Hat OpenShift.

How to Modernize Your Platform and Run Production AI on Azure Red Hat OpenShift — Source: azure.microsoft.com

What You Need

An Azure subscription with permissions to create and manage resources.
Access to Azure Red Hat OpenShift (provisioned via the Azure portal or CLI).
Familiarity with Red Hat OpenShift concepts (clusters, namespaces, operators).
Understanding of Azure identity and security services (Azure Active Directory, Azure Policy, Azure Security Center).
AI/ML workload requirements (models, data pipelines, scaling needs).
A governance framework for managing multiple AI initiatives (optional but recommended).

Step-by-Step Guide

Step 1: Assess Your Current Environment and Modernization Goals

Begin by evaluating your existing infrastructure, application portfolio, and AI maturity. Identify which workloads are candidates for modernization—either through rehosting, refactoring, or rebuilding. Key questions: Are you running legacy applications that need a more scalable, secure foundation? Do you have AI pilots that you want to move to production? Understand your regulatory and compliance requirements, especially if you operate in a highly regulated sector like finance. This assessment will inform the design of your Azure Red Hat OpenShift environment.

Step 2: Provision Azure Red Hat OpenShift as Your Foundation

Deploy an Azure Red Hat OpenShift cluster using the Azure portal, CLI, or ARM templates. Choose the appropriate size and region based on workload demands and latency requirements. ARO is jointly operated and supported by Microsoft and Red Hat, ensuring a stable, certified Kubernetes platform. Configure networking, storage, and access controls. This step creates the secure, scalable base that Banco Bradesco used to host its enterprise AI platform.

Step 3: Integrate Azure Identity, Security, and Policy Capabilities

To achieve consistent governance across all AI initiatives, integrate your ARO cluster with Azure services:

Azure Active Directory for single sign-on and role-based access control (RBAC).
Azure Policy to enforce organizational rules on configurations, resource types, and compliance.
Azure Security Center and Azure Sentinel for threat detection and security monitoring.
Azure Key Vault to manage secrets, certificates, and encryption keys used by AI models.

This integration layer ensures that every workload benefits from enterprise-grade security without manual overhead.

Step 4: Migrate or Build AI Workloads on the Platform

With your ARO cluster and Azure integrations in place, start moving AI workloads to production. You can either migrate existing containerized applications or build new AI pipelines using OpenShift’s built-in CI/CD tools (e.g., OpenShift Pipelines, ArgoCD). Use operators for machine learning frameworks (e.g., Kubeflow, TensorFlow) and leverage Azure services like Azure Machine Learning for model training and deployment. Banco Bradesco unified more than 200 AI initiatives on this foundation, demonstrating that scale is achievable.

Step 5: Implement Governance Across All AI Initiatives

Central to the success of production AI is governance. Use Azure Policy and OpenShift’s multi-tenancy features (namespaces, quotas, network policies) to enforce consistent rules across all AI projects. Establish chargeback mechanisms, audit logs, and compliance dashboards. By treating each AI initiative as a managed workload within a shared platform, you avoid siloed governance and reduce operational risk.

Step 6: Scale and Optimize for Production

Once workloads are running, monitor performance and cost using Azure Monitor and OpenShift metrics. Implement auto-scaling (horizontal pod autoscaler, cluster autoscaler) to handle demand spikes. Continuously optimize resource allocation, security posture, and governance policies. The joint support from Microsoft and Red Hat means you can escalate issues quickly and benefit from regular platform updates.

Tips for Success

Start with a pilot. Choose one or two AI initiatives to move to production on ARO before scaling to hundreds.
Leverage the joint support model. Both Microsoft and Red Hat provide integrated support—use it to troubleshoot and optimize.
Invest in automation. Use Infrastructure as Code (Terraform, Bicep) to provision and manage ARO clusters consistently.
Focus on identity-first security. Integrate Azure AD and Role-Based Access Control early to avoid rework.
Engage with the ecosystem. Learn from partners like Banco Bradesco and consider applying for the Red Hat Ecosystem Innovation Awards to showcase your success.
Plan for compliance. If your industry has strict regulations, use Azure Policy to enforce controls from day one.

Tags: