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For a long time, AI was mainly a playground for researchers and scientists. The focus was on creating tools for researchers to build better models and explore new areas rather than operationalizing AI. To make AI work in the real world, we must focus on security, responsibility, scalability, and reproducibility, and these are the same principles we apply to any software development project.

Red Hat OpenShift AI provides an integrated MLOps platform for building, training, tuning, deploying and monitoring AI-enabled applications and predictive and foundation models at scale across hybrid cloud environments. This solution accelerates AI/ML innovation, drives AI/ML operational consistency, and promotes transparency and flexibility when implementing AI solutions across the organization. Red Hat OpenShift AI builds on top of Red Hat OpenShift to deliver a consistent, streamlined, and automated experience when handling the workload and performance demands of AI/ML projects.

To operationalize AI in an enterprise, you can benefit from open source tools, containerization, and DevOps principles applied to AI. This is often referred to as MLOps. Red Hat specializes in these three pillars, providing world-class open source tooling and platforms.

Learn how to develop and deploy AI applications

To bring better AI/ML to organizations, Red Hat has released the new Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) course. This course teaches you how to train, deploy, and serve models, including how to use Red Hat OpenShift AI to apply best practices in machine learning and data science. You also learn to create, run, manage, and troubleshoot data science pipelines so you can automate AI workflows.

Additionally, for those interested in Red Hat’s first-ever OpenShift AI Certification, we're releasing Red Hat Certified Specialist in OpenShift AI (EX267) later in 2024. This new exam will accompany the course and test your ability to deploy OpenShift AI and configure it to build, deploy, and manage machine learning models to integrate into AI-enabled applications. By passing this exam, you become a Red Hat Certified Specialist in OpenShift AI, which also counts towards earning a Red Hat Certified Architect (RHCA).

Having experience in Red Hat OpenShift is required, so it's recommended that you complete Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) before moving on to AI267.

Get started with OpenShift AI

Red Hat Training and Certification provides a comprehensive curriculum designed to empower both new and experienced professionals in AI/ML. Our curriculum is tailored to equip teams and individuals with the necessary skills and knowledge to thrive in today’s dynamic IT landscape. If you have any questions or would like to stay up to date with our latest exam, connect with our learning experts, who are ready to assist you in a personalized learning journey that suits your professional or organizational goals.

If you're new to all of this, then you can get a no cost introduction to operationalizing AI/ML with Red Hat OpenShift AI in our Red Hat OpenShift AI Technical Overview (AI067) course. This on-demand video covers how Red Hat OpenShift AI builds on the capabilities of Red Hat OpenShift to provide a single, consistent, enterprise-ready hybrid AI and MLOps platform. You'll be well on your way to the new Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) course in no time.

Sobre o autor

Karl Reynolds is the Senior Director of Red Hat Training and Certification, based out of Raleigh, North Carolina. In this role, he provides strategic direction and leads the sales and delivery of new Red Hat Training and Certification offerings globally.

Reynolds has nearly 25 years of training and technology experience. He has spent the last ten years serving Red Hat in varying director-level roles, and is a multi-time recipient of the Red Hat President’s Club Award.

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