AI-253 Python and OpenShift AI Certification Training Course
Length
5 days / 5 weeks
Price
$3999
Days
Mon - Fri
Why Choose This Course
AI253 Creating Machine Learning Models with Python and Red Hat OpenShift AI is an instructor-led course that teaches the essentials of Python and foundational machine learning while showing how to train models using Red Hat OpenShift AI. Learners begin with Python fundamentals such as syntax, data types, functions, modules, object‑oriented programming, exceptions, and debugging. These core programming skills support typical AI and data science tasks like automation, data handling, and basic analysis. The course then introduces key machine learning concepts and model training workflows, positioning Python as the primary language for building and experimenting with models.
The training connects these skills to enterprise operations on Red Hat OpenShift AI. Participants work with data science projects, workbenches, and data connections and useJupyterNotebooks to execute and test code interactively. The focus is on practical model training within OpenShift AI, using recommended practices for organizing code and resources so teams can iterate quickly andprepare fordownstream tasks such as model serving and pipeline automation in related courses. The course content is described to be based on Python 3, Red Hat Enterprise Linux 9, Red Hat OpenShift 4.14, and Red Hat OpenShift AI 2.8, reflecting contemporary platform capabilities used in production environments.
This course is relevant for data scientists, developers, andMLOpsengineersbuildingAI solutions in hybrid cloud settings. Learners gain a clear understanding of the OpenShift AI architecture and hands‑on practice training models on the platform, which helps them contribute to AI projects that require collaborative notebooks,governedresource use, and repeatable workflows. The course avoids absolute promises and instead emphasizes exam‑aligned content where applicable, hands‑on practice, and real‑world applicability of Python and OpenShift AI to common model development activities. A certificate of course attendance is included.
Prerequisites
- Experience with Git is required. Experience with Red Hat OpenShift is required, or completion of DO288 Red Hat OpenShift Developer II. Basic experience in AI, data science, or machine learning is recommended.
Exam
There is no certification exam associated with this AI253 course.
Books
- Ai253 course material included.
Delivery
- Live virtual online training attend in real-time from anywhere
Skills Gained
- Describe Python core syntax, data types, and operations for AI tasks
- Use Python collections including lists, sets, tuples, and dictionaries
- Write functions and structure programs with modules and namespaces
- Apply object‑oriented programming with classes and objects
- Handle exceptions and debug using the Python debugger (pdb)
- Read and write files and parse JSON in Python workflows
- Use regular expressions in Python to process text data
- Explain basic machine learning concepts and common learning types
- Train machine learning models using OpenShift AI workbenches
- Apply recommended practices when training models with OpenShift AI
- Organize AI code and data with data science projects and workbenches
- Execute and test code interactively using Jupyter Notebooks
Audience
- Data scientists and AI practitioners building and training models on OpenShift AI
- Developers integrating machine learning into applications
- MLOps engineers managing and monitoring AI/ML workloads on OpenShift AI
Course Schedule & Pricing
Choose the schedule that fits your life — all options include full course materials & certification support
Full-time immersion for rapid certification readiness.
Balance your career while you upgrade your skills.
Maximum flexibility for busy working professionals.
Outline
- Introduction to Python
- Setting up the developer environment
- Basic Python syntax and semantics
- Control flow features and operators
- Collections: lists, sets, tuples, dictionaries
- Functions and program decomposition
- Modules and namespaces for code organisation
- Classes and object‑oriented programming
- Exceptions and error handling
- Input and output with files
- Parsing and generating JSON
- Debugging with the Python debugger (pdb)
- Introduction to machine learning concepts
- Types of machine learning and common workflows
- Data preparation fundamentals for model training
- Training models with default and custom workbenches
- Enhancing model training using OpenShift AI capabilities
- Organising code using data science projects
- Working with workbenches and data connections
- Using Jupyter Notebooks for interactive development
- Running and testing AI/ML code on OpenShift AI
- Applying best practices for reproducible ML experiments
Terms & Conditions
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Our Partnership
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