How to Build a Production-Ready AI Platform with Azure Red Hat OpenShift: A Step-by-Step Guide

Introduction

At Red Hat Summit 2026, Microsoft and Red Hat showcased how their joint offering, Microsoft Azure Red Hat OpenShift, enables organizations to move artificial intelligence from experimental pilots into fully operational, production-grade systems. This guide, inspired by the accomplishments of Banco Bradesco—a major Latin American financial institution honored alongside Microsoft as the Red Hat Ecosystem Innovation Award winner for Platform Modernization—will walk you through building a secure, scalable AI foundation that unifies governance across hundreds of initiatives. Follow these steps to transform your platform modernization journey and achieve consistent identity, security, and scale.

What You Need

• Azure subscription with permissions to create resources (or access to an existing environment).
• Red Hat OpenShift expertise within your team or a trusted partner.
• Azure identity and security services such as Azure Active Directory (now Microsoft Entra ID), Azure Policy, and Azure Security Center.
• AI workload artifacts (models, data pipelines, container images) ready for deployment.
• Governance framework outlining compliance, regulatory, and security requirements.
• Commitment to open, enterprise-grade platforms and a willingness to adopt a jointly supported solution.

Step-by-Step How-To Guide

Step 1: Assess Your Modernization and AI Goals

Start by analyzing your current infrastructure and identifying where AI can deliver measurable outcomes. Banco Bradesco began this journey with a clear focus on moving beyond proof-of-concept to production, while adhering to strict financial regulations. Document your legacy systems, containerization needs, and the specific AI initiatives you plan to support—aim for a realistic roadmap that includes 10 to 200+ projects. Define success metrics such as improved customer experience, operational efficiency, or risk reduction.

Step 2: Deploy Azure Red Hat OpenShift as Your Foundation

Provision an Azure Red Hat OpenShift cluster using the Azure portal or CLI. This managed Kubernetes platform combines Microsoft Azure’s cloud services with Red Hat OpenShift’s enterprise container orchestration. Ensure you configure high availability, networking, and storage according to your scale requirements. Banco Bradesco chose Azure Red Hat OpenShift because it provides a secure, scalable base that integrates seamlessly with Azure’s ecosystem. Test the cluster with a small workload before moving to production.

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

For production AI, consistent identity and governance are nonnegotiable. Connect your cluster to Azure Active Directory (Microsoft Entra ID) to enforce role-based access control. Apply Azure Policy to ensure compliance with regulatory standards, and enable Azure Security Center for threat detection. This step mirrors what Banco Bradesco did to unify security across their AI initiatives, ensuring that each model and pipeline operates under a single governance umbrella. Verify integration by testing user authentication and policy enforcement.

Step 4: Unify Governance Across AI Initiatives

Establish centralized governance by defining policies that apply to all AI workloads running on Azure Red Hat OpenShift. Use Red Hat OpenShift’s built-in cluster resource quotas, network policies, and audit logs alongside Azure Policy. For Banco Bradesco, this unified governance allowed them to manage over 200 AI projects consistently, meeting regulatory and security requirements without slowing innovation. Create a policy library and assign roles to teams, ensuring every AI initiative adheres to the same standards for data privacy, model versioning, and access controls.

Step 5: Migrate and Scale AI Workloads to Production

With the foundation in place, migrate your AI pilots to the cluster. Containerize models using Red Hat OpenShift’s built-in CI/CD pipelines or integrate with Azure DevOps. Scale workloads horizontally by leveraging Kubernetes auto-scaling and Azure’s elastic resources. Banco Bradesco moved beyond experimentation by deploying AI models that handle real-time transactions and customer interactions. Monitor performance using Azure Monitor and Red Hat OpenShift’s dashboards, and adjust resources as needed to maintain service-level agreements.

Step 6: Continuously Optimize with Joint Support

Take advantage of the joint support provided by Microsoft and Red Hat for Azure Red Hat OpenShift. This means you get a single point of contact for issues spanning both Azure and OpenShift. Regularly review your cluster’s performance, security posture, and cost efficiency. Learn from the ecosystem: Microsoft’s recognition as Platform Modernization Partner of the Year proves that organizations like Banco Bradesco achieve outstanding results through collaboration. Schedule quarterly reviews with your partner or internal team to identify new opportunities for AI-driven modernization.

Tips for Success

• Start with a clear business case—just like Banco Bradesco, define the value of moving AI to production early and communicate it across departments.
• Invest in training for your operations and development teams to master Azure Red Hat OpenShift and its integration with Azure services.
• Leverage the Red Hat Ecosystem Innovation Award insights as a benchmark; Microsoft’s success as Platform Modernization Partner shows that enterprise-grade platforms deliver measurable results.
• Plan for governance from day one—enforce consistent identity and policy before scaling to hundreds of AI initiatives to avoid rework.
• Use joint support effectively to resolve issues faster and stay aligned with both Microsoft and Red Hat roadmaps.

By following these steps and tips, your organization can replicate the success seen at Red Hat Summit 2026, turning AI pilots into secure, scalable production systems that drive real business outcomes.