Red Hat OpenShift AI Roadshow
Build, train, and deploy AI-enabled applications into production faster with a hybrid MLOps platform

About event:
The OpenShift AI roadshow gives attendees insight into how organizations are using, delivering, monitoring AI-enabled applications with speed across the hybrid cloud. As part of the roadshow – IT operations, data engineers, data scientists, and application developers will have the opportunity to build, train, and deploy an AI model and integrate it 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:

• AI/ML market, technology, and operational trends that will help inform your organization’s AI/ML initiatives.
• How a hybrid MLOps platform - and its partner ecosystem - eases collaboration between IT, data science, and application development teams to enable faster innovation.
• Use case and implementation lessons other organizations have learned from starting or optimizing their AI/ML software application delivery process.
• Hands-on experience working with a MLOps platform. Key lessons include working with data science projects and workbenches, training different models and measuring accuracy, serving and deploying models, integrating different models and capabilities into a frontend application, and testing the application as a user. No AI experience is required.

Who should attend:

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

Know before you go

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.