Redhat Technologies

EX267: Red Hat OpenShift AI

Learn Red Hat OpenShift AI to build, deploy, and manage AI/ML models across cloud, on-premises, and edge environments. Gain hands-on experience with open-source tools, streamline AI workflows, and enhance your career as an AI/ML Engineer, Data Scientist, or Cloud AI professional.

Ram Prakash Updhayay - KR Network Cloud Devops Trainer
Mr. Ram Upadhyay

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    What is Red Hat OpenShift AI?

    Red Hat OpenShift AI is an advanced AI platform designed to enable data scientists and developers to rapidly develop, train, deploy, and monitor machine learning (ML) models across multiple environments, including on-site, public cloud, and edge. 

    The platform offers seamless collaboration between data science teams and developers, helping them transition from experimental phases to production in a streamlined manner. 

    This course provides a deep understanding of Red Hat OpenShift AI, empowering learners to utilize the platform effectively for AI-driven application development.

    Objective

    Learning Objectives

    • Understand OpenShift AI Platform: Gain a deep understanding of how Red Hat OpenShift AI functions as a powerful tool for AI/ML model lifecycle management.
    • Develop and Deploy AI-Models: Learn how to develop, train, deploy, and serve machine learning models in a consistent environment.
    • Use Open-Source Tooling: Get hands-on experience with popular open-source tools integrated into the platform, such as Jupyter, TensorFlow, PyTorch, and more.
    • Optimize AI Workflows: Learn to streamline AI workflows from experimentation to production across hybrid environments.
    • Monitor and Maintain AI Models: Understand how to monitor, fine-tune, and maintain deployed models for optimal performance across various environments.

    This course equips participants with the knowledge needed to build scalable AI solutions using Red Hat OpenShift AI’s robust capabilities, ensuring efficient collaboration between data scientists and developers.

    Course Content

    MODULE 1 - INTRODUCTION
    • Developing and Deploying AI/ML Applications on Red Hat OpenShift AI
    • Orientation to the Classroom Environment
    • Performing Lab Exercises
    MODULE 2 - INTRODUCTION TO RED HAT OPENSHIFT AI
    • Introduction to Red Hat OpenShift AI
    • Architecture
    • Summary
    MODULE 3 - DATA SCIENCE PROJECTS
    • Data Science Projects
    • Guided Exercise: Data Science Projects
    • Workbenches
    • Guided Exercise: Workbenches
    • Data Connections
    • Guided Exercise: Data Connections
    • Summary
    MODULE 4 - JUPYTER NOTEBOOKS
    • Introduction to Jupyter Notebooks
    • Guided Exercise: Introduction to Jupyter Notebooks
    • Collaboration with Jupyter Notebooks
    • Guided Exercise: Collaboration with Jupyter Notebooks
    • Summary
    MODULE 5 - INSTALLING RED HAT OPENSHIFT AI
    • Red Hat OpenShift AI Installation
    • Guided Exercise: Red Hat OpenShift AI Installation
    • Summary
    MODULE 6 - MANAGE USERS AND RESOURCES
    • Create and Import a Custom Notebook Image
    • Guided Exercise: Create a Custom Notebook Image
    • Summary
    MODULE 7 - CUSTOM NOTEBOOK IMAGES
    • Create and Import a Custom Notebook Image
    • Guided Exercise: Create a Custom Notebook Image
    • Summary
    MODULE 8 - INTRODUCTION TO MACHINE LEARNING
    • Machine Learning Concepts
    • Quiz: Machine Learning Concepts
    • Machine Learning Workflow
    • Summary
    MODULE 9 - INTRODUCTION TO MACHINE LEARNING
    • Machine Learning Concepts
    • Quiz: Machine Learning Concepts
    • Machine Learning Workflow
    • Summary
    MODULE 9 - TRAINING MODELS
    • Training ML Models
    • Guided Exercise: Training ML Models
    • Training Models with Custom Workbenches
    • Guided Exercise: Training Models with Custom Workbenches
    MODULE 10 - ENHANCING MODEL TRAINING WITH RHOAI
    • Inspecting Workbench Resources
    • Guided Exercise: Inspecting Workbench Resources
    • Scaling Data Loading
    • Guided Exercise: Scaling Data Loading
    • Monitoring the Training Process
    • Guided Exercise: Monitoring the Training Process
    • Software Engineering Principles for Data Science
    • Guided Exercise: Software Engineering Principles for Data Science
    • Summary
    MODULE 11 - INTRODUCTION TO MODEL SERVING
    • Concepts and Key Aspects of Serving AI Models
    • Quiz: Concepts and Key Aspects of Serving AI Models
    • Saving Trained Machine Learning Models
    • Guided Exercise: Saving Trained Machine Learning Models
    • Serving Models as Stand-Alone Applications
    • Guided Exercise: Serving Models as Stand-alone Applications
    • Summary
    MODULE 12 - MODEL SERVING IN RED HAT OPENSHIFT AI
    • Using Model Servers to Deploy Models
    • Guided Exercise: Using Model Servers to Deploy Models
    • Consuming the Model Serving API
    • Guided Exercise: Consuming the Model Serving API
    • Creating and Using Model Servers
    • Guided Exercise: Creating and Using Model Servers
    • Summary
    MODULE 13 - INTRODUCTION TO DATA SCIENCE PIPELINES
    • Concepts of Data Science Pipelines
    • Quiz: Concepts of Data Science Pipelines
    • Create a Pipeline Server
    • Guided Exercise: Creating a Pipeline Server
    • Summary
    MODULE 14 - ELYRA PIPELINES
    • Creating Pipelines with Elyra
    • Guided Exercise: Creating Pipelines with Elyra
    • Summary
    MODULE 15 - KUBEFLOW PIPELINES
    • Creating Pipelines with Kubeflow Pipelines
    • Guided Exercise: Creating Pipelines with Kubeflow Pipelines
    • Summary

    Why Learn This New Technology?

    This technology is a platform for organizations looking to leverage AI and machine learning at scale. By mastering this platform, learners can build, deploy, and manage AI models more efficiently in hybrid cloud environments.

    With increasing reliance on AI across industries, this course provides essential skills to stay competitive, automate processes, and create intelligent applications that can operate on-premises or in the cloud.

    Learning OpenShift AI also empowers professionals to integrate popular open-source tools and streamline the AI model lifecycle for better business outcomes.

    Are you eligible for this training?

    This course is ideal for:

    • Data Scientists: Professionals looking to deploy and manage AI/ML models effectively across various environments.
    • AI/ML Developers: Developers seeking to collaborate with data scientists to implement AI solutions.
    • Cloud Engineers: Engineers managing cloud infrastructure who want to incorporate AI solutions into their workflows.
    • IT Professionals: Those looking to expand their expertise in AI-enabled applications and automation.

    Prerequisites:

    • A basic understanding of Linux operating systems and container orchestration (such as Kubernetes or OpenShift).
    • Familiarity with cloud computing environments (AWS, Azure, or other).
    • Knowledge of AI/ML concepts and experience working with Python, Jupyter Notebooks, or other data science tools is beneficial but not mandatory.

    Career Opportunities After Completing This Course:
     After completing the Red Hat OpenShift AI course, participants can explore roles such as:

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    Course Details
    • Mr. Ram Upadhyay
    • Course Duration
      32 Hours
    • Batch Type
      Online Live Classes With Zoom Meeting
    • Email Address
      info@krnetworkcloud.org
    • Contact No.
      +91 9555378418
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