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Develop, Test, and Run Granite Family LLMs with Red Hat Enterprise Linux AI (AI296 Course Overview

Develop, Test, and Run Granite Family LLMs with Red Hat Enterprise Linux AI (AI296 Course Overview

The AI296 course offered by Koenig Solutions is designed to provide learners with a comprehensive understanding of artificial intelligence and its practical applications in real-world scenarios. Participants will explore key topics such as machine learning, data analysis, and the integration of AI technologies across various industries.

The primary learning objectives include developing the ability to implement AI solutions, understanding algorithmic functions, and analyzing data for meaningful insights. By the end of the course, students will be equipped with practical skills that enable them to tackle complex challenges, enhancing their career opportunities in the evolving tech landscape. Join us to elevate your expertise in AI and become a valuable asset in your field!

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  • Live Training (Duration : 24 Hours)
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Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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Koenig is is awarded as Red Hat's Enterprise Partner with the Highest YoY Growth for CY-23!

Course Prerequisites

Certainly! Here are the minimum required prerequisites for successfully undertaking training in the AI296 course:

  • A basic understanding of AI & ML is recommended but not required.
  • Familiarity with the Linux command line.

These prerequisites are designed to help you succeed in the course, and while a solid foundation in the above areas is beneficial, the course aims to support your learning journey!

Target Audience for AI296

AI296 is an advanced course focusing on artificial intelligence and machine learning, designed for professionals seeking to enhance their expertise in AI technology and its applications.


  • Data Scientists
  • Machine Learning Engineers
  • AI Researchers
  • Software Developers
  • IT Managers
  • Business Analysts
  • Product Managers
  • Systems Architects
  • Tech Entrepreneurs
  • Digital Transformation Leaders


Learning Objectives - What you will Learn in this AI296?

AI296 Course Overview

The AI296 course is designed to equip participants with essential skills in artificial intelligence and machine learning, focusing on practical applications and effective problem-solving strategies.

Learning Objectives and Outcomes

  • Understand key concepts and frameworks of artificial intelligence.
  • Implement machine learning algorithms using relevant programming languages.
  • Analyze data patterns to derive actionable insights.
  • Develop practical AI solutions for real-world problems.
  • Evaluate the performance of AI models and optimize their accuracy.
  • Utilize deep learning techniques for advanced data analysis.
  • Explore ethical considerations and implications of AI.
  • Collaborate on AI projects, fostering teamwork and communication.
  • Identify trends and future directions in AI technology.
  • Gain hands-on experience through practical labs and exercises.

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