Duration: 32 Hrs
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 RedHat 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 RedHat OpenShift®4.14,and RedHat OpenShift AI 2.8.
OpenShift AI Certification:-
Certification Preparation > 4 Hours
- OpenShift AI certification equips you with the skills to efficiently manage and deploy AI workloads, optimizing resources and improving scalability in real-world applications.
- Gain expertise in integrating AI solutions with containerized environments, enhancing your ability to streamline machine learning workflows and automate complex processes.
Recommended training
- Experience with Git is required
- Experience in Python development is required
- Experience in RedHat OpenShift is required,or completion of The RedHat OpenShift DeveloperII:Building and Deploying Cloud-native Applications (DO288)course
- Basic experience in the AI, data science, and machine learning fields is recommended
Course Outline
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Introduction to RedHat OpenShift AI
Identify the main features of RedHat OpenShift AI,and describe the architecture and components of Red Hat OpenShift AI.
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Data Science Projects
Organize code and configuration by using data science projects, workbenches, and data connections
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JupyterNotebooks
Use Jupyter note books to execute and test code interactively
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Installing RedHat OpenShift AI
Installing RedHat OpenShift AI by using the web console and the CLI, and managing Red Hat OpenShift AI components
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Managing Users and Resources
Managing RedHat OpenShift AI users, and resource allocation for Workbenches
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Custom Note book Images
Creating custom notebook images,and importing a custom notebook through the Red Hat OpenShift AI dashboard
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Introduction to Machine Learning
Describe basic machine learning concepts,different types of machine learning, and machine learning workflows
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Training Models
Train models by using default and custom workbenches
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Enhancing Model Training with RHOAI
Use RHOAI to apply best practices in machine learning and data science
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Introduction to Model Serving
Describe the concepts and components required to export, share and serve trained machine learning models
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Model Serving in RedHat OpenShift AI
Serve trained machine learning models with OpenShift AI
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CustomModelServers
Deploy and serve machine learning models by using custom model serving runtimes
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Introduction to Data Science Pipelines
Create,run,manage,and troubleshoot data science pipelines
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Elyra Pipelines
Creating a DataScience Pipeline with Elyra
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Kube Flow Pipelines
Creating a DataScience Pipeline with KubeFlow SDK