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Developing and Deploying AI/ML Applications on Red Hat OpenShift AI Self-paced
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Virtual
Duration
1 Day
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R24 900,00
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Introduction
Self-paced training presents dynamic text-based content and on-demand videos that feature our most experienced Red Hat certified instructors, that gives you the feel of an in-classroom setting without incurring travel costs and risking time away from the office. It includes unlimited access to course content and 80 hours of cloud-based labs, all optimized for self-study. Give your mind the knowledge you need to advance your skills, prepare for certification exams, and further your career.
- Same high-quality content used in our classrooms
- 80 hours of cloud-based labs
- Knowledge checks after every unit, validating your comprehension
- Presentations from our top instructors delivered in HD video
- Videos are automatically added to the course as they are made available
- Videos translated into multiple languages with subtitles
- 90-days of unlimited access
- Downloadable e-books for offline viewing
- Full and searchable transcripts
- Email support
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.
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 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
Delegates should be able to demonstrate the following skills:
- Understand the foundations of the Red Hat OpenShift AI architecture
- Install Red Hat OpenShift AI
- Manage resource allocations
- Update components and manage users and their permissions
- Train, deploy and serve models, including how to use Red Hat OpenShift AI to apply best practices in machine learning and data science
- Define and set up data science pipelines with Red Hat OpenShift AI
Course content
| Lesson 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 |
| Lesson 2: Data Science Projects |
| Organize code and configuration by using data science projects, workbenches, and data connection |
| Lesson 3: Jupyter Notebooks |
| Use Jupyter notebooks to execute and test code interactively |
| Lesson 4: Red Hat OpenShift AI Installation |
| Install Red Hat OpenShift AI and manage Red Hat OpenShift AI components |
| Lesson 5: User and Resource Management |
| Manage Red Hat OpenShift AI users and allocate resources |
| Lesson 6: Custom Notebook Images |
| Create and import custom notebook images in Red Hat OpenShift AI |
| Lesson 7: Introduction to Machine Learning |
| Describe basic machine learning concepts, different types of machine learning, and machine learning workflows |
| Lesson 8: Training Models |
| Train models by using default and custom workbenches |
| Lesson 9: Enhancing Model Training with RHOAI |
| Use RHOAI to apply best practices in machine learning and data science |
| Lesson 10: Introduction to Model Serving |
| Describe the concepts and components required to export, share and serve trained machine learning models |
| Lesson 11: Model Serving in Red Hat OpenShift AI |
| Serve trained machine learning models with OpenShift AI |
| Lesson 12: Introduction to Data Science Pipelines |
| Define and set up Data Science Pipelines |
| Lesson 13: Working with Pipelines |
| Create data science pipelines with the Kubeflow SDK and Elyra |
| Lesson 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®).
Audience for this exam:
- System and Software Architectswho need to demonstrate an understanding of the features and functionality of Red Hat OpenShift AI.
- System Administratorsor developers who need to demonstrate the ability to configure, support and maintain OpenShift AI.
- Data Scientistswho need to demonstrate an understanding of using OpenShift AI to develop, train, serve, test, and monitor AI/ML models and applications.
- Red Hat Certified Engineerswho wish to become a Red Hat Certified Architect (RHCA)
Red Hat Overview
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