Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)
An introduction to developing and deploying AI/ML applications on Red Hat OpenShift AI.
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.
As a result of attending this course, you will understand the foundations of the Red Hat OpenShift AI architecture. You will be able to install Red Hat OpenShift AI, manage resource allocations, update components and manage users and their permissions. You will also be able to train, deploy and serve models, including hot to use Red Hat OpenShit AI to apply best practices in machine learning and data science. Finally you will be able to create, run, manage and troubleshoot data science pipelines.
Data scientists and AI practitioners who want to use Red Hat OpenShift AI to build and train ML models Developers who want to build and integrate AI/ML enabled applications MLOps engineers responsible for installing, configuring, deploying, and monitoring AI/ML applications on Red Hat OpenShift AI.
- Experience with Git is required Experience in Python development is required, or completion of the Python Programming with Red Hat (AD141) course
- Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course
- Basic experience in the AI, data science, and machine learning fields is recommended
- Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI.
- Organize code and configuration by using data science projects, workbenches, and data connections
- Use Jupyter notebooks to execute and test code interactively
- Installing Red Hat OpenShift AI by using the web console and the CLI, and managing Red Hat OpenShift AI components
- Managing Red Hat OpenShift AI users, and resource allocation for Workbenches
- Creating custom notebook images, and importing a custom notebook through the Red Hat OpenShift AI dashboard
- Describe basic machine learning concepts, different types of machine learning, and machine learning workflows
- Train models by using default and custom workbenches
- Use RHOAI to apply best practices in machine learning and data science
- Describe the concepts and components required to export, share and serve trained machine learning models
- Serve trained machine learning models with OpenShift AI
- Deploy and serve machine learning models by using custom model serving runtimes
- Create, run, manage, and troubleshoot data science pipelines
- Creating a Data Science Pipeline with Elyra
- Creating a Data Science Pipeline with KubeFlow SDK