Introduction to Python Programming and to Red Hat OpenShift AI (AI252)
An introduction to Python programming, and creating and managing AI/ML workloads with Red Hat OpenShift AI.
Python is a popular programming language used by system administrators, data scientists, and developers to create applications, perform statistical analysis, and train AI/ML models. This course introduces the Python language and teaches the basics of using Red Hat OpenShift AI for AI/ML workloads. This course helps students build core skills such as describing the Red Hat OpenShift AI architecture, and organizing, executing and testing AI/ML code through hands-on experience. These skills can be applied in all versions of Red Hat OpenShift AI.
This course is based on Python 3, RHEL 9.0, Red Hat OpenShift ® 4.14, and Red Hat OpenShift AI 2.8.
- Basics of Python syntax, functions and data types
- How to debug Python scripts using the Python debugger (pdb)
- Use Python data structures like dictionaries, sets, tuples and lists to handle compound data
- Learn Object-oriented programming in Python and Exception Handling
- How to read and write files in Python and parse JSON data
- How to effectively structure large Python programs using modules and namespaces
- Introduction to Red Hat OpenShift AI
- Data Science Projects
- Jupyter Notebooks
- Data scientists and AI practitioners who want to use Red Hat OpenShift AI to build and train ML models
- Developers who want to build and integrate AI/ML enabled applications
- MLOps engineers responsible for installing, configuring, deploying, and monitoring AI/ML applications on Red Hat OpenShift AI
- Experience with Git is required
- Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building Kubernetes Applications (DO288) course
- Basic experience in the AI, data science, and machine learning fields is recommended
- Introduction to Python and setting up the developer environment
- Explore the basic syntax and semantics of Python
- Understand the basic control flow features and operators
- Write programs that manipulate compound data using lists, sets, tuples and dictionaries
- Decompose your programs into composable functions
- Organize your code using Modules for flexibility and reuse
- Explore Object Oriented Programming (OOP) with classes and objects
- Handle runtime errors using Exceptions
- Implement programs that read and write files
- Use advanced data structures like generators and comprehensions to reduce boilerplate code
- Read and write JSON data
- Debug Python programs using the Python debugger (pdb)
- Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat OpenShift AI.
- Organize code and configuration by using data science projects, workbenches, and data connections
- Use Jupyter notebooks to execute and test code interactively