category
Developing and Deploying AI/ML Applications on Red Hat OpenShift AI
Type
Virtual
Skill Level
Available dates
Learning Path
Virtual
Duration
1 Day
TYPE
Virtual
LEARNING PATH
SKILL LEVEL
DURATION
AVAILABLE DATES
Introduction
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.
Audience profile
- 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
- Developers, data scientists, and AI practitioners who want to automate their ML workflows
- MLOps engineers responsible for operationalizing the ML lifecycle on Red Hat OpenShift AI
Pre-requisites
- 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
Course objectives
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 how to use Red Hat OpenShift AI to apply best practices in machine learning and data science. Finally you will be able to define and set up data science pipelines with Red Hat OpenShift AI.
Course content
| Session 1: Introduction to Red Hat OpenShift AI |
| Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat AI. |
| Session 2: Data Science Projects |
| Organize code and configuration by using data science projects, workbenches, and data connections. |
| Session 3: Jupyter Notebooks |
| Use Jupyter notebooks to execute and test code interactively. |
| Session 4: Red Hat OpenShift AI Installation |
| Install Red Hat OpenShift AI and manage Red Hat OpenShift AI components. |
| Session 5: User and Resource Management |
| Manage Red Hat OpenShift AI users and allocate resources. |
| Session 6: Custom Notebook Images |
| Create and import custom notebook images in Red Hat OpenShift AI. |
| Session 7: Introduction to Machine Learning |
| Describe basic machine learning concepts, different types of machine learning, and machine learning workflows. |
| Session 8: Training Models |
| Train models by using default and custom workbenches. |
| Session 9: Enhancing Model Training with RHOAI |
| Use RHOAI to apply best practices in machine learning and data science. |
| Session 10: Introduction to Model Serving |
| Describe the concepts and components required to export, share and serve trained machine learning models. |
| Session 11: Model Serving in Red Hat OpenShift AI |
| Serve trained machine learning models with OpenShift AI. |
| Session 12: Introduction to Data Science Pipelines |
| Define and set up Data Science Pipelines. |
| Session 13: Working with Pipelines |
| Create data science pipelines with the Kubeflow SDK and Elyra. |
| Session 14: Controlling Pipelines and Experiments |
| Configure, monitor, and track pipelines with artifacts, metrics, and experiments. |
Associated certifications and exam
The Red Hat Certified Specialist in OpenShift AI (EX267) exam 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 that also counts towards earning a Red Hat Certified Architect (RHCA).
Red Hat Overview
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