Red Hat Certification

Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)

  • Live instructor-led course
  • Internship with live projects
  • Industry-relevant curriculum
  • Online and Offline training available
  • Placement support until you get placed

This course introduces students to developing and deploying AI/ML applications on Red Hat OpenShift AI, providing fundamental knowledge and hands-on experience with OpenShift AI to train, develop, and deploy machine learning models.

  • Intermediate
  • English
Course Description

Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267) provides students with the fundamental knowledge about using Red Hat OpenShift for developing and deploying AI/ML applications. This course helps students build core skills for using Red Hat OpenShift AI to train, develop, and deploy machine learning models through hands-on experience.

This course is based on Red Hat OpenShift 4.14 and Red Hat OpenShift AI 2.8.

Note: This course is offered as a 3-day in-person class, a 4-day virtual class, or is self-paced. Durations may vary based on the delivery.

The Red Hat Certified Specialist in OpenShift AI exam (EX267) tests candidates' ability to deploy OpenShift AI and configure it to build, deploy, and manage machine learning models to support 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®). This exam is based on Red Hat OpenShift AI version 2.8 and Red Hat OpenShift Container Platform version 4.14.

What you’ll learn
  • Understand the foundations of Red Hat OpenShift AI architecture.
  • Install OpenShift AI, manage resource allocations, update components, and manage users and permissions.
  • Train, deploy, and serve models, and apply best practices in machine learning and data science using OpenShift AI.
  • Create, run, manage, and troubleshoot data science pipelines.
Introduction to Red Hat OpenShift AI - Identify the main features of Red Hat OpenShift AI and describe its architecture and components.
Data Science Projects - Organize code and configuration using data science projects, workbenches, and data connections.
Jupyter Notebooks - Use Jupyter notebooks to execute and test code interactively.
Installing Red Hat OpenShift AI - Install OpenShift AI using the web console and CLI, and manage OpenShift AI components.
Managing Users and Resources - Manage OpenShift AI users and resource allocation for workbenches.
Custom Notebook Images - Create custom notebook images and import custom notebooks through the OpenShift AI dashboard.
Introduction to Machine Learning - Describe basic machine learning concepts, types, and workflows.
Training Models - Train models using default and custom workbenches.
Enhancing Model Training with RHOAI - Apply best practices in machine learning and data science using RHOAI.
Introduction to Model Serving - Describe the concepts and components required to export, share, and serve trained machine learning models.
Model Serving in Red Hat OpenShift AI - Serve trained machine learning models with OpenShift AI.
Custom Model Servers - Deploy and serve machine learning models using custom model serving runtimes.
Introduction to Data Science Pipelines - Create, run, manage, and troubleshoot data science pipelines.
Elyra Pipelines - Create a Data Science Pipeline with Elyra.
KubeFlow Pipelines - Create a Data Science Pipeline with KubeFlow SDK.
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Frequently Asked Questions

Experience with Git, Python development, and Red Hat OpenShift is required. Basic experience in AI, data science, and machine learning is recommended.

The course utilizes Red Hat OpenShift AI and various data science tools and platforms.

This course is available in-person, virtually, or as self-paced learning. The teaching mode includes lectures and hands-on labs.
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