4 days virtual course
Developing and Deploying AI/ML applications on Red Hat OpenShift AI [AI267LS]
An introduction to developing and deploying AI/ML applications on Red Hat OpenShift AI
This course is based on Red Hat OpenShift ® 4.18, and Red Hat OpenShift AI 2.25.
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Red Hat Learning Subscription Course is a 365-day subscription product that combines self-paced training, live virtual classes, and certification exams to deliver an enhanced and flexible learner experience.
Available through Red Hat Learning Subscription Course: Purchase the AI267 subscription to gain access to Red Hat’s virtual training offerings and schedule your preferred session within your active subscription period.
Skills gained
- Introduction to Red Hat OpenShift AI
- Data Science Projects
- Jupyter Notebooks
- Red Hat OpenShift AI Installation
- Users and Resources Management
- Custom Notebook Images
- Introduction to Machine Learning
- Training Models
- Enhancing Model Training with RHOAI
- Introduction to Model Serving
- Model Serving? in Red Hat OpenShift AI
- Introduction to Data Science Pipelines
- Working with Pipelines
- Controlling Pipelines and Experiments
Target Audience
- 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?
Prerequisites
- Experience with Git is required
- Experience in Python development 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
Content
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.
Data Science Projects
- Organize code and configuration by using data science projects, workbenches, and data connections
Jupyter Notebooks
- Use Jupyter notebooks to execute and test code interactively
Red Hat OpenShift AI Installation
- Install Red Hat OpenShift AI and manage Red Hat OpenShift AI components
User and Resource Management
- Manage Red Hat OpenShift AI users and allocate resources
Custom Notebook Images
- Create and import custom notebook images in Red Hat OpenShift AI
Introduction to Machine Learning
- Describe basic machine learning concepts, different types of machine learning, and machine learning workflows
Training Models
- Train models by using default and custom workbenches
Enhancing Model Training with RHOAI
- Use RHOAI to apply best practices in machine learning and data science
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
Introduction to Data Science Pipelines
- Define and set up Data Science Pipelines
Working with Pipelines
- Create data science pipelines with the Kubeflow SDK and Elyra
Controlling Pipelines and Experiments
- Configure, monitor, and track pipelines with artifacts, metrics, and experiments
Form
- Delivery methods: Self-paced + virtual, live instructor-led (1 virtual class per purchased course)
- Exam + retake: 1 exam + 1 retake and exam readiness tools
- Hands-on labs: 100 hours per course
Do you have any questions please contact
- Charlotte Heimann
- Seniorspecialist
- +45 72203147