Course

RAI252

Introduction to Python Programming and to Red Hat OpenShift AI («RAI252»)

An introduction to Python programming, and creating and managing AI/ML workloads with Red Hat OpenShift AI.
Duration 4 days
Price 3'300.–
Note: This course is offered as a 4-day in-person class or a 5-day virtual class. Durations may vary based on the delivery.
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Course facts

  • Basics of Python syntax, functions and data types
  • Debugging Python scripts using the Python debugger (pdb)
  • Using Python data structures like dictionaries, sets, tuples and lists to handle compound data
  • Learning object-oriented programming in Python and Exception Handling
  • Reading and writing files in Python and parsing JSON data
  • Effectively structuring large Python programs using modules and namespaces
  • Introduction to Red Hat OpenShift AI
  • Data Science Projects
  • Jupyter Notebooks

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.

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.
  • 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
  • Basic experience in the AI, data science, and machine learning fields is recommended
  • Experience in Red Hat OpenShift is required, or completion of the following course:

This course is based on Python 3, RHEL 9.0, Red Hat OpenShift ® 4.14, and Red Hat OpenShift AI 2.8.

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