MLflow & Agent Observability

Interactive demo•1 min• Red Hat Enterprise Linux

Published on May 10, 2026 by Roberto Carratalá

Explore how MLflow supports AgentOps by helping you evaluate and compare AI model performance. This demo shows how to navigate experiments, review metrics, and analyze runs - making it easier to understand and improve your models.
A 3D RHEL platform icon, next to a cloud and a targeted cursor
Next steps Resources Related interactive demos Feedback

Next steps

Overview
Overview
Blog post
E-book
E-book
Interactive demo

LLM Compression & Model Optimization

Learn how the LLM Compressor reduces resource usage and accelerates inference by optimizing large language models for enterprise environments.

3 mins

Red Hat OpenShift

 Interactive demo

MCP Server for RHEL

Learn how MCP server enables natural language monitoring and log analysis for RHEL systems. See AI-powered performance monitoring and log analysis for Red Hat Enterprise Linux.

2 mins

Red Hat Enterprise Linux

We'd like to hear from you

We welcome your thoughts on our interactive demos at Red Hat. We're committed to delivering engaging, hands-on learning resources, and your input helps us continue to improve our offerings.

Share feedback
A woman wearing a Red Hat hoodie and headphones smiles while using a laptop

About the author of this page

Roberto Carratalá, Cloud Services Black Belt, Red Hat headshot

Roberto Carratalá

Principal AI Architect - AI BU

Roberto is a Principal AI Architect working in the AI Business Unit specializing in Container Orchestration Platforms (OpenShift & Kubernetes), AI/ML, DevSecOps, and CI/CD. With over 10 years of experience in system administration, cloud infrastructure, and AI/ML, he holds two MSc degrees in Tel...