Developing and Deploying AI/ML Applications on Red Hat OpenShift AI (AI267)

Price
$3,210.00 USD

Duration
5 Days

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

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 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.

This course is based on Red Hat OpenShift ® 4.14, and Red Hat OpenShift AI 2.8.

Course Objectives

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 hot to use Red Hat OpenShit AI to apply best practices in machine learning and data science. Finally you will be able to create, run, manage and troubleshoot data science pipelines.

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 MLOps engineers responsible for installing, configuring, deploying, and monitoring AI/ML applications on Red Hat OpenShift AI

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Course Prerequisites

Experience with Git is required Experience in Python development is required, or completion of the Python Programming with Red Hat (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

Agenda

1. 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.

2. Data Science Projects

Organize code and configuration by using data science projects, workbenches, and data connections

3. Jupyter Notebooks

Use Jupyter notebooks to execute and test code interactively

4. Installing Red Hat OpenShift AI

Installing Red Hat OpenShift AI by using the web console and the CLI, and managing Red Hat OpenShift AI components

5. Managing Users and Resources

Managing Red Hat OpenShift AI users, and resource allocation for Workbenches

6. Custom Notebook Images

Creating custom notebook images, and importing a custom notebook through the Red Hat OpenShift AI dashboard

7. Introduction to Machine Learning

Describe basic machine learning concepts, different types of machine learning, and machine learning workflows

8. Training Models

Train models by using default and custom workbenches

9. Enhancing Model Training with RHOAI

Use RHOAI to apply best practices in machine learning and data science

10. Introduction to Model Serving

Describe the concepts and components required to export, share and serve trained machine learning models

11. Model Serving in Red Hat OpenShift AI

Serve trained machine learning models with OpenShift AI

12. Custom Model Servers

Deploy and serve machine learning models by using custom model serving runtimes

13. Introduction to Workflow Automation

Create, run, manage, and troubleshoot data science pipelines

14. Elyra Pipelines

Creating a Data Science Pipeline with Elyra
15. KubeFlow Pipelines
- Creating a Data Science Pipeline with KubeFlow SDK