4 days virtual course
Developing and Deploying AI/ML applications on Red Hat OpenShift AI [AI267VT]
An introduction to developing and deploying AI/ML applications on Red Hat OpenShift AI
This course is based on Red Hat OpenShift ® 4.16, and Red Hat OpenShift AI 2.13.
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
Instructor
This training is provided by Red Hat authorized instructor. The training will be virtual in English.
Do you have any questions please contact
- Charlotte Heimann
- Seniorspecialist
- +45 72203147