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About this class

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.

What you'll be able to do

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

Course outline

1 - An Overview of Python 3

Introduction to Python and setting up the developer environment

2 - Basic Python Syntax

Explore the basic syntax and semantics of Python

3 - Language Components

Understand the basic control flow features and operators

4 - Collections

Write programs that manipulate compound data using lists, sets, tuples and dictionaries

5 - Functions

Decompose your programs into composable functions

6 - Modules

Organize your code using Modules for flexibility and reuse

7 - Classes in Python

Explore Object Oriented Programming (OOP) with classes and objects

8 - Exceptions

Handle runtime errors using Exceptions

9 - Input and Output

Implement programs that read and write files

10 - Data Structures

Use advanced data structures like generators and comprehensions to reduce boilerplate code

11 - Parsing JSON

Read and write JSON data

12 - Debugging

Debug Python programs using the Python debugger (pdb)

13 - Introduction to Red Hat OpenShift AI

Identify the main features of Red Hat OpenShift AI, and describe the architecture and components of Red Hat OpenShift AI.

14 - Data Science Projects

Organize code and configuration by using data science projects, workbenches, and data connections

15 - Jupyter Notebooks

Use Jupyter notebooks to execute and test code interactively

Before you attend

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

Who this is for

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