Transforming AI from Pilot to Production with Azure Red Hat OpenShift: A Step-by-Step Guide

Introduction

Red Hat Summit 2026 showcased how Microsoft and Red Hat are helping organizations bridge the gap between AI experimentation and production deployment. The key enabler? Microsoft Azure Red Hat OpenShift (ARO) — a fully managed, jointly supported platform that combines Red Hat OpenShift's container orchestration with Azure's enterprise services. This guide walks you through the practical steps to modernize your platform and move AI initiatives from proof-of-concept to scalable, governed production systems. Drawing from real-world success like Banco Bradesco, you'll learn to unify identity, security, and policy across hundreds of AI projects.

Transforming AI from Pilot to Production with Azure Red Hat OpenShift: A Step-by-Step Guide — Source: azure.microsoft.com

What You Need

An active Azure subscription with permissions to create ARO clusters
Red Hat account (for OpenShift access and support)
Basic familiarity with Kubernetes and containerized workloads
Understanding of your organization's regulatory and security requirements
A small batch of pilot AI applications to start with
Azure identity and governance tools (Microsoft Entra ID, Azure Policy, Azure Security Center)
Optional: existing CI/CD pipelines or GitOps tooling

Step 1: Assess Your AI Maturity and Define Production Requirements

Before adopting any platform, evaluate where your AI projects stand. Are you still running isolated experiments? Do you have multiple models in development without consistent governance? List your current AI initiatives and note their operational needs: scaling, compliance, data residency, and identity management. For example, Banco Bradesco — a major Latin American financial institution — had over 200 AI initiatives requiring unified governance across strict regulatory boundaries. Use this audit to define critical success factors for a production platform.

Inventory all AI pilots and their stages
Identify security and compliance requirements (e.g., banking regulations, data privacy laws)
Determine scalability targets (number of models, users, concurrent workloads)
Map existing identity and policy tools (Azure Active Directory, Azure Policy)

Step 2: Choose Azure Red Hat OpenShift as Your Unified Platform

Next, select a platform that can handle both modernization and AI at scale. Azure Red Hat OpenShift provides a consistent Kubernetes foundation managed jointly by Microsoft and Red Hat. It integrates natively with Azure services — including identity, security, and monitoring — while delivering Red Hat's enterprise-grade OpenShift capabilities. This combination eliminates the gap between development and operations, enabling teams to focus on value rather than infrastructure. Start by provisioning an ARO cluster in your desired Azure region. Consult the official ARO documentation for setup steps.

Create a new ARO cluster via Azure Portal, CLI, or ARM template
Configure networking (virtual network, subnets, DNS)
Connect your Red Hat OpenShift subscription for updates and support
Set up node pools for AI workloads (GPU-enabled if needed)

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

The key to production AI is not just running models — it's operating them with consistent governance. Merge your ARO cluster with Azure's identity layer (Microsoft Entra ID) for unified authentication and authorization. Apply Azure Policy to enforce compliance rules across all namespaces and workloads. Use Azure Security Center or Defender for Cloud to monitor threats. This integration, as demonstrated by Banco Bradesco, ensures that all 200+ AI initiatives adhere to the same security standards and access controls. For step-by-step integration, refer to the Azure AD integration guide.

Enable Azure AD authentication for your ARO cluster
Define role-based access control (RBAC) for teams and models
Create Azure Policy assignments for security baselines (e.g., encryption, network policies)
Enable monitoring with Azure Monitor and Container Insights

Step 4: Establish a Governance Model for Multi-Team AI Development

Production AI requires clear ownership, versioning, and lifecycle management. Use OpenShift's project namespaces to isolate each AI initiative. Implement GitOps principles (e.g., with Argo CD or OpenShift Pipelines) to manage deployments consistently. Integrate your CI/CD pipelines with Azure DevOps or GitHub Actions. Set up policies for model promotion (from dev to staging to production). This governance framework allows you to scale from a handful of pilots to hundreds of initiatives without chaos.

Organize AI projects into OpenShift namespaces with quotas
Apply GitOps for infrastructure and application code
Define a model registry or use Azure Machine Learning integration
Automate testing and validation before moving models to production

Step 5: Scale from Pilots to Production with Joint Microsoft-Red Hat Support

Once your governance is in place, migrate your most promising AI pilots onto ARO. Use the platform's scaling capabilities (horizontal pod autoscaling, cluster autoscaler) to handle varying workloads. Leverage Azure services like Azure Machine Learning for model training and deployment, all running on the same ARO cluster. The joint support from Microsoft and Red Hat means you have a single contact for issue escalation — as highlighted by the Red Hat Ecosystem Innovation Award recognition for platform modernization. Banco Bradesco's move from experimentation to production on ARO is a testament to this partnership's reliability.

Start with one or two high-value pilots for initial migration
Monitor performance and cost using Azure Cost Management and OpenShift metrics
Iterate on security and compliance based on audit findings
Scale gradually by adding more AI initiatives as confidence grows

Tips for Success

Start with a clear outcome: Focus on a specific business problem (e.g., fraud detection or customer personalization) rather than generic AI.
Involve security teams early: Integrate identity and policy from day one to avoid retrofitting later.
Invest in GitOps automation: It reduces human error and speeds up deployments across teams.
Leverage joint support: Both Microsoft and Red Hat offer combined support channels — use them for architecture reviews and incident handling.
Benchmark against leaders: Learn from Banco Bradesco's approach: unify governance, enforce compliance, and use a single platform for all AI workloads.
Plan for scaling: Design your namespace and policy structure to accommodate future growth without rework.

Conclusion

Platform modernization for production AI doesn't have to be complex. By following these steps — assessing your needs, choosing Azure Red Hat OpenShift, integrating Azure identity and security, establishing governance, and scaling with joint support — you can transform AI experiments into reliable, secure, and scalable production systems. The recognition of Microsoft as Red Hat's Platform Modernization Partner of the Year underscores the value of this approach. Start your journey today and see why Banco Bradesco and others are moving from pilots to production with confidence.

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