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

Introduction

At Red Hat Summit 2026, Microsoft and Red Hat showcased how Microsoft Azure Red Hat OpenShift enables organizations to transition artificial intelligence (AI) initiatives from experimental pilots to fully operational production systems. This guide draws on real-world success—like Banco Bradesco, a major Latin American financial institution—to provide a clear, actionable roadmap. By following these steps, you can harness Azure Red Hat OpenShift’s unified governance, security, and scalability to modernize your platform and run AI workloads at scale, end-to-end.

What You Need

• An active Microsoft Azure subscription with permissions to create and manage resources.
• Access to Azure Red Hat OpenShift—a jointly managed, enterprise-grade Kubernetes cluster.
• Red Hat OpenShift CLI (oc) or Azure CLI for cluster management.
• Existing AI models or pipelines (e.g., machine learning models, containerized applications).
• Azure identity and security services—such as Azure Active Directory and Azure Policy—for integration.
• A cross-functional team including DevOps, security, data science, and platform engineering.
• Basic familiarity with Kubernetes concepts (pods, services, namespaces).

Step-by-Step Guide

Step 1: Assess Your Current AI Pilot and Platform Readiness

Before scaling, evaluate where you are now. Review your existing AI pilots: Are they isolated proofs-of-concept or loosely coupled experiments? Identify gaps in identity, governance, and security. For example, Banco Bradesco operated over 200 AI initiatives—each requiring consistent oversight. Use this checklist:

• List all active AI projects and their infrastructure.
• Document current security policies and compliance requirements.
• Determine whether pilots can be containerized and migrated to OpenShift.

This baseline ensures you understand the scope of modernization needed.

Step 2: Design a Unified Governance Framework on Azure Red Hat OpenShift

Central to production AI is consistent governance. Azure Red Hat OpenShift integrates natively with Azure services. Configure Azure Policy to enforce compliance across all namespaces and projects. Use Azure Role-Based Access Control (RBAC) with Azure Active Directory to manage user identities. Define:

• Role assignments for data scientists, operators, and security teams.
• Policy rules for resource quotas, allowed container images, and network restrictions.
• Audit logging via Azure Monitor and OpenShift’s audit logs.

This step transforms a collection of disjointed pilots into a single governed platform.

Step 3: Migrate AI Workloads to Azure Red Hat OpenShift

With governance in place, move your AI workloads. Containerize models and dependencies using Docker. Create deployment manifests (YAML) for each service. Push images to Azure Container Registry (ACR) and deploy to OpenShift clusters:

1. Set up a CI/CD pipeline (e.g., using Azure DevOps or GitHub Actions) to build and test containers.
2. Use oc apply or Helm charts to deploy to OpenShift.
3. Enable auto-scaling based on CPU, memory, or custom metrics via OpenShift’s horizontal pod autoscaler.

Banco Bradesco used this approach to unify over 200 AI initiatives—a key step from pilot to production.

Step 4: Integrate Azure Identity and Security Services

Production AI requires airtight security. Integrate Azure Identity with OpenShift:

• Azure Active Directory (AAD) as the identity provider for OpenShift authentication.
• Managed Identities for workloads to access Azure resources securely.
• Azure Key Vault to store secrets (e.g., model keys, database passwords).
• Azure Policy to enforce compliance across the cluster.

These integrations ensure that every AI workload inherits enterprise-grade security without manual intervention.

Step 5: Scale AI Workloads with Consistent Operations

Once migrated, optimize for production. Use OpenShift’s built-in monitoring (Prometheus and Grafana) and Azure Monitor. Set up alerting for model drift, resource exhaustion, or anomalies. Implement rolling updates and canary deployments to minimize downtime. For example, Banco Bradesco’s platform now handles massive transaction volumes while maintaining strict regulatory controls—production AI on a jointly supported platform.

Step 6: Continuously Improve with Feedback Loops

Production is not static. Establish feedback loops from models in production back to data science teams. Use Azure Machine Learning integration with OpenShift to retrain models based on real-world data. Automate A/B testing of new model versions within the same OpenShift cluster. This iterative process mirrors the Red Hat Ecosystem Innovation Award recognition for platform modernization.

Tips for Success

• Start small: Migrate one critical AI pilot first to validate the governance framework before scaling.
• Leverage the joint support model: Microsoft and Red Hat co-manage Azure Red Hat OpenShift, so use their combined expertise for complex issues.
• Embrace infrastructure as code: Use Terraform or ARM templates to provision clusters and policies repeatably.
• Monitor costs – use Azure Cost Management + Billing to track OpenShift resource consumption per project.
• Document everything: Include runbooks for incident response and updates to the governance policy.
• Engage the community: Red Hat and Microsoft offer extensive documentation, webinars, and partner ecosystems—tap into them.

By following these steps, your organization can follow the lead of award-winning adopters like Banco Bradesco and transition from AI experimentation to robust, production-grade deployments. Ready to start? Begin with Step 1: Assess Your Current State.