Automation unleashed: Introducing the new Red Hat Certified Ansible Collection amazon.ai for generative AI

Automation unleashed: Introducing the new Red Hat Certified Ansible Collection amazon. ai for generative AI The problem: Manual AI management doesn’t scale Introducing the amazon. ai Collection Core capabilities of Red Hat AnsibleCertified Collection for amazon. ai Automating generative AI with Amazon Bedrock Managing operational performance with DevOps Guru Why Red Hat AnsibleCertified Collection for amazon. ai matters to you Get started with the Red Hat AnsibleCertified Collection for amazon. ai Automation for your AI infrastructure Red Hat Ansible Automation Platform | Product Trial About the author Alina Buzachis More like this Red Hat Lightspeed 2025: From observability to actionable automation Setting up logrotate in Linux Technically Speaking | Taming AI agents with observability Transforming Your Secrets Management | Code Comments Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Generative AI demands infrastructure that’s not only powerful but repeatable, auditable, and scalable. From chat bots and content generation to intelligent automation agents, organizations are deploying AI at scale. But with this innovation comes complexity. In short, deploying generative AI isn’t just about models, it’s about managing the infrastructure and operations behind them reliably. The Red Hat Certified Collection, amazon. ai , addresses this problem by bringing infrastructure-as-code principles to AI and operational monitoring. Even with powerful services like Amazon Bedrock and DevOps Guru, organizations face hurdles: Agent lifecycle complexity : Bedrock Agents can orchestrate multiple models and APIs, but creating, updating, and validating these agents manually is tedious and prone to error.