Course

Developing and Deploying AI/ML Applications on Red Hat OpenShift AI («RAI267»)

Name: Developing and Deploying AI/ML Applications on Red Hat OpenShift AI («RAI267»)
Price: 2700 CHF

An introduction to developing and deploying AI/ML applications on Red Hat OpenShift AI.

Duration 3 days

Price 2'700.–

Note: This course is offered as a 3-day in-person class, a 4-day virtual class or is self-paced. Durations may vary based on the delivery.

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Course facts

Key Learnings

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

Content

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. 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.
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.

1 Data Science Projects
Organize code and configuration by using data science projects, workbenches, and data connections

2 Jupyter Notebooks
Use Jupyter notebooks to execute and test code interactively

3 Red Hat OpenShift AI Installation
Install Red Hat OpenShift AI and manage Red Hat OpenShift AI components

4 User and Resource Management
Manage Red Hat OpenShift AI users and allocate resources

5 Custom Notebook Images
Create and import custom notebook images in Red Hat OpenShift AI

6 Introduction to Machine Learning
Describe basic machine learning concepts, different types of machine learning, and machine learning workflows

7 Training Models
Train models by using default and custom workbenches

8 Enhancing Model Training with RHOAI
Use RHOAI to apply best practices in machine learning and data science

9 Introduction to Model Serving
Describe the concepts and components required to export, share and serve trained machine learning models

10 Model Serving in Red Hat OpenShift AI
Serve trained machine learning models with OpenShift AI

11 Introduction to Data Science Pipelines
Define and set up Data Science Pipelines

12 Working with Pipelines
Create data science pipelines with the Kubeflow SDK and Elyra

13 Controlling Pipelines and Experiments
Configure, monitor, and track pipelines with artifacts, metrics, 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

Requirements

Experience with Git is required
Basic experience in the AI, data science, and machine learning fields is recommended
Experience in Python development is required, or completion of the following course:
Experience in Red Hat OpenShift is required, or completion of the following course: