Who should attend
- 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, or completion of the Red Hat Training Presents: Introduction to Python Programming (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
Impact on the Organization Organizations collect and store vast amounts of information from multiple sources. With Red Hat OpenShift AI, organizations have a platform ready to analyze data, visualize trends and patterns, and predict future business outcomes by using machine learning and artificial intelligence algorithms.
Impact on the Individual 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.
Follow On Courses
- Red Hat Certified Specialist in OpenShift AI (EX267)
Follow On Courses
Course Content
Course Content Summary
- 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