Creating Machine Learning Models with Python and Red Hat OpenShift AI Training

Last Updated: 18 08 2025

This course provides a wide introduction to Python programming and foundational machine learning concepts, which prepares learners to create and deploy intelligent solutions.

Designed for the aspiration of data scientists and developers, it examines the formation of machine learning models using the role of Python in data analysis and practical, hands-on laboratories on the hands. Participants will understand supervised, unsafe, and reinforcement learning, and the Red Hat OpenShift AI will receive exposure for workflows training.

The learners will dive into the real-world dataset, train models efficiently, and implement neural network and deep learning techniques to solve complex problems. Emphasis is placed on adopting model evaluation, feature engineering, and moral AI practices. The course also touches on the importance of scalability and continuous improvement by introducing online machine learning methods.

Threat through Red Hat OpenShift AI, participating data from data ingestion to deployment, will practice the management of the ML life cycle. This training gives students the right to produce production-ready models by mastering both the Python coding skills and the platform-specific equipment that supports enterprise AI development.

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Learning Options for You

  • Live Training (Duration : 40 Hours)
  • Per Participant

Fee: On Request

Course Prerequisites

Before enrolling, participants should be familiar with basic programming concepts, preferably in Python, as well as fundamental statistics. Prior exposure to containers and cloud technologies is helpful but not strictly necessary. The course is designed to introduce OpenShift AI, so beginners to container orchestration can still benefit as long as they are willing to learn. 

  • Basic knowledge of Python programming (mandatory) 
  • Understanding of basic statistics and data analysis concepts (recommended) 
  • Familiarity with container or cloud environments (helpful but not required) 

Learning Objectives

This course aims to provide learners with a strong foundation in building machine learning models using Python, while leveraging Red Hat OpenShift AI for scalable, containerized deployment. Participants will explore the essential steps in the machine learning pipeline — from data preparation to model training and evaluation — and learn how to operationalise these models in a production-ready OpenShift environment. By the end of the course, learners will be able to build, deploy, and manage basic machine learning solutions more efficiently and effectively. 

Target Audience

  • Data analysts and data scientists 
  • Software developers interested in AI and ML 
  • DevOps and MLOps engineers 
  • IT professionals exploring containerised ML workflows 
  • Students and professionals looking to get started with machine learning 
  • Anyone interested in deploying ML models with Red Hat OpenShift AI 

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