A half-day experience, the Red Hat AI roadshow gives you insight into how to accelerate time to value of AI-enabled applications that scale across hybrid cloud. So you can increase team productivity, enhance customer experiences, and drive innovation with enterprise-ready AI, including generative and predictive AI. You’ll also see how you can measure the business impact of an AI initiative.

As part of the roadshow attendees will have the opportunity to build, train, and deploy AI models and integrate them into an application with a hands-on lab. They’ll experience what it is like to go from an idea to building an accurate model to having a production-ready application. 

What you’ll learn and experience:

• What it takes to build a secure, scalable AI-enabled application and show the business impact it makes 
• How generative AI is being used as a strategic enabler by enterprises 
• How Red Hat AI accelerates AI innovation and reduces the operational cost of developing and delivering AI solutions across hybrid cloud environments
• How to get consistent, fast and cost-effective inference
• Why and how to align models to private enterprise data
• Innovating with Agentic AI
• Approaches to other common AI obstacles: AI safety & security, serving private models to internal users, and measuring value of AI
• Hands-on experience working with a MLOps platform: Build and train different model types, integrate them into a frontend application to create new features, and test the application as a user. No AI experience is required

• AI engineer 
• Data engineers
• Data scientists
• ML engineer
• IT decision makers
• IT directors
• Infrastructure architects
• Infrastructure specialists
• Enterprise architects
• Developers
• Application architects
• Enterprise architects
• Developer team leads

Operations, developers, and data science practitioners doing the hands-on lab should have these suggested tools and knowledge of these areas: 

• A laptop computer running Windows, MacOS, or Linux with the Firefox or Chrome web browser.
• Entry-level Kubernetes concepts
• A general understanding of Linux containers (e.g., Docker, CRI-O, etc.).
• General knowledge of AI terminology (e.g., machine learning, deep learning, foundation models, etc.).
• No AI experience is required.