Opis: 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.

Note: This course is offered as a 4 day in person class or a 5 day virtual class.

Course Content Summary

• 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

Cel: Impact on the Organization

• Organizations collect and store vast amounts of information from multiple sources. With Red Hat OpenShift AI, organizations have a platform ready to analyze data, visualize trends and patterns, and predict future business outcomes by using machine learning and artificial intelligence algorithms.

Impact on the Individual

• As a result of attending this course, you will understand the foundations of the Red Hat OpenShift AI architecture. You will be able to organize code and configuration by using data science projects, workbenches, and data connections. You will also be able to execute and test code interactively by using Jupyter notebooks. This course is the starting point for the AI/ML learning path in which you will learn how to create and maintain AI/ML workflows.

Grupa docelowa:

• 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

Uwagi: Duration – 4 Days Classroom,  5 Days VT

Recommended next course or exam

• Red Hat OpenShift AI Administration (AI263)

Technology considerations:

• No ILT classroom will be available

Wymagania:

• Experience with Git is required
• Experience in Red Hat OpenShift is required, or completion of the Red Hat OpenShift Developer II: Building and Deploying Cloud-native Applications (DO288) course
• Basic experience in the AI, data science, and machine learning fields is recommended

Konspekt: 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

Notyfikacja: Note: The course outline is subject to change as technology advances and the underlying job evolves. For questions or confirmation on a specific objective or topic, please contact us at osec@osec.pl