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

  • Length 4 days
Course overview
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Why study this course

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.

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What you’ll learn

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 create, run, manage and troubleshoot data science pipelines.


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Who is the course for?

  • 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


Course subjects

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

Jupyter Notebooks
Use Jupyter notebooks to execute and test code interactively

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

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

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

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 modelsI

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

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

Introduction to Data Science Pipelines
Create, run, manage, and troubleshoot data science pipelines

Elyra Pipelines
Creating a Data Science Pipeline with Elyra

KubeFlow Pipelines
Creating a Data Science Pipeline with KubeFlow SDK


Prerequisites


THIRD PARTY REGISTRATION

Lumify Work offers certification and training in Enterprise Linux, Ansible, JBoss, OpenShift, OpenStack, and more through our partnership with Red Hat. This arrangement requires Lumify Work to provide your details to Red Hat for course and/or exam registration purposes.


Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.


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