Red Hat AI Roadshow Workshop

OverviewA 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 makesHow generative AI is being used as a strategic enabler by enterprisesHow Red Hat AI accelerates AI innovation and reduces the operational cost of developing and delivering AI solutions across hybrid cloud environmentsHow to get consistent, fast and cost-effective inferenceWhy and how to align models to private enterprise dataInnovating with Agentic AIApproaches to other common AI obstacles: AI safety & security, serving private models to internal users, and measuring value of AIHands-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. Who should attend:AI engineerData engineers Data scientistsML engineerIT decision makersIT directorsInfrastructure architectsInfrastructure specialistsEnterprise architectsDevelopersApplication architectsDeveloper team leadsKnow before you goOperations, 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 conceptsA 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.