AI253 Creating Machine Learning Models with Python and Red Hat OpenShift AI Course
AI253 Creating Machine Learning Models with Python and Red Hat OpenShift AI Training
The Red Hat AI253 course provides an introduction to Python programming and foundational machine learning concepts, using Red Hat OpenShift AI to support model training and deployment. Designed for system administrators, developers, and data professionals, this course focuses on practical skills for building and managing AI/ML workflows in real-world environments.
Participants will learn key Python programming techniques including syntax, data structures, and debugging. AI253 also covers core machine learning topics such as supervised and unsupervised learning, data preparation, and model training best practices, all within the OpenShift AI platform.
By the end of the course (AI253), learners will be able to develop Python-based applications, work with machine learning models, and use Red Hat OpenShift AI to support scalable and efficient AI solutions. Training is based on Python 3, RHEL 9.0, OpenShift 4.14, and OpenShift AI 2.8, and is available in Melbourne, Sydney, Brisbane, Adelaide, Canberra, Perth, Hobart, and online across Australia.
- Basic Git familiarity is necessary.
- Requires knowledge of Red Hat OpenShift or completion of the DO288 course.
- Experience in artificial intelligence (AI), data science, and machine learning is preferred.
There is no certification exam associated with this course.
Creating Machine Learning Models with Python and Red Hat OpenShift AI Course material provided.
- Understand the foundations of Red Hat OpenShift AI architecture.
- Organize code and configuration using data science projects, workbenches, and data connections.
- Execute and test code interactively using Jupyter notebooks.
- Learn the basics of AI/ML workflow creation and maintenance.
- Data scientists and AI professionals seeking to create and train ML models with Red Hat OpenShift AI.
- Developers looking to design and deploy AI/ML-enabled applications.
- MLOps engineers are in charge of delivering, administering, and monitoring AI/ML applications using Red Hat OpenShift AI.
- Python Basics: Understand basic syntax, control flow, and operators.
- Data Collections: Work with lists, sets, tuples, and dictionaries.
- Functions & Modules: Write reusable code and organize it effectively.
- Object-Oriented Programming (OOP): Learn about classes, objects, and encapsulation.
- Error Handling: Manage runtime errors using exceptions.
- File Handling: Read and write data using input and output operations.
- Advanced Data Structures: Utilize generators and comprehensions for efficient coding.
- JSON Handling: Parse and manipulate JSON data.
- Debugging: Use the Python debugger (pdb) to troubleshoot programs.
- Introduction to Machine Learning: Learn basic ML concepts, types, and workflows.
- Training ML Models: Train models using default and custom workbenches.
- Enhancing Model Training: Apply best practices in AI/ML with Red Hat OpenShift AI (RHOAI).
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This training course does not have any exam associated with it
Course material in digital format is included for flexibility and ease of use
Practise questions are provided for better understanding of the key concepts
Attend the course with an instructor at our training centre or from anywhere
Relax, we will beat competitor’s advertised price. Our course has no extra costs
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The supply of this course/package/program is governed by our terms and conditions. Please read them carefully before enrolling, as enrolment is conditional on acceptance of these terms and conditions. Proposed course dates are given, course runs subject to availability and minimum registrations.
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