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

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4658 Learners

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.

Redhat training

Key Features

Course content summary

    • Introduction to Red Hat OpenShift AI
    • Data Science Projects
    • Jupyter Notebooks
    • Installing Red Hat OpenShift AI
    • Managing Users and Resources
    • 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
    • Custom Model Servers
    • Introduction to Workflow Automation
    • Elyra Pipelines
    • KubeFlow Pipelines

Audience for this course

    • 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

Prerequisites for this course

    • 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

Technology considerations

    • No ILT classroom will be available

Training Options

Self-Paced eLearning

On Demand
  • Lifetime access to recorded self-paced eLearning course created by industry experts
  • 3 simulation mock test papers for Practice
  • 24x7 learner assistance and support

Corporate Training

Customized
  • Live Instructor Led / Campus delivery model
  • Flexible pricing options according to your requirements
  • Pre & Post evaluation test for comparison
  • 24x7 support & assistance

Outline

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

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 models

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 Workflow Automation

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

 

Outcome

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 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.

Why Innovative Technology Solutions

Our Virtual Instructor Led Training model brings classroom learning experience online. With our world-class LMS instructor-led training, self-paced E-learning and personalized mentoring you will get an immersive first-class learning experience.

Self-Paced E-learning

A self-paced e-learning with recorded video sessions that you can access anytime without going beyond your comfort zone.

Live Virtual Classroom

An interactive classroom style virtual instructor led training to engage and learn more alongside your peers with a live trainer.

Learner's Community

A Social forum where you can ask your questions, one of our expert will reply you within 24 hours on that community.

Online Access of Labs

Online access of practise labs that you can access anytime, anywhere your machine.

Industry Based Projects

Real-time Industry based projects will be shared by the trainers throughout the program

24/7 Assistance

Get engaged with integrated support assistance on your desktop and mobile learning