Overview
The Creating Machine Learning Models with Python and Red Hat OpenShift AI course provides a comprehensive introduction to Python programming, basic machine learning concepts, and practical training on using Red Hat OpenShift AI for building and deploying ML models. Participants will gain hands-on experience in Python programming, understanding machine learning workflows, and applying best practices in AI/ML model training using Red Hat OpenShift AI. This course is ideal for data scientists, developers, and MLOps engineers looking to enhance their skills in AI/ML development.
Objectives
By the end of this course, leaner will be able to:
- Understand and apply Python syntax, functions, and data types.
- Learn the basics of machine learning concepts and different types of machine learning.
- Train ML models using default and custom workbenches.
- Apply best practices in AI/ML model training using Red Hat OpenShift AI.
- Debug Python scripts and handle runtime errors effectively.
Prerequisites
- Experience with Git is required for version control and collaboration.
- Experience in Red Hat OpenShift or completion of the Red Hat OpenShift Developer II course is necessary.
- Basic understanding of AI, data science, and machine learning is recommended.
- Familiarity with Python programming is helpful, though not required.
- Ability to set up and configure the Python development environment is needed.
Course Outline
- Overview of Python and its use cases.
- Setting up the development environment for Python programming.
- Learning Python syntax, functions, and data structures.
- Understanding control flow features and operators.
- Exploring basic machine learning concepts and workflows.
- Understanding different types of machine learning.
- Training ML models using Python and Red Hat OpenShift AI.
- Utilizing default and custom workbenches for model training.
- Applying best practices in AI/ML using Red Hat OpenShift AI.
- Managing and optimizing ML workloads on the platform.