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Free Pre-requisite TrainingJoin 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 ReportsReceive 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 RecordingsGet 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 ExtensionsExtend 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
Prerequisites for Advanced Techniques in Large Language Model Development and Deployment:
To successfully undertake training in the Advanced Techniques in Large Language Model Development and Deployment course, we recommend that students possess the following minimum required knowledge:
Basic Understanding of Machine Learning Concepts: Familiarity with fundamental machine learning principles, such as supervised learning, unsupervised learning, and model evaluation.
Programming Skills: Proficiency in programming languages commonly used in machine learning, particularly Python.
Experience with Neural Networks: Basic knowledge of neural network architectures and how they are trained, which will be built upon throughout the course.
Familiarity with Data Preprocessing Techniques: Understanding of basic data preprocessing steps such as tokenization and text embedding.
Introductory Knowledge of Cloud Platforms: Awareness of cloud services, such as Microsoft Azure, and basic skills in setting up and managing cloud infrastructure.
These prerequisites ensure that our students have a solid foundation to build upon, maximizing their learning experience and enabling them to effectively grasp the advanced techniques covered in the course.
The Advanced Techniques in Large Language Model Development and Deployment course is tailored for IT professionals looking to enhance their expertise in machine learning, responsible AI, and cloud deployment strategies.
1. Introduction: The "Advanced Techniques in Large Language Model Development and Deployment" course is a comprehensive 3-day training designed to equip students with the skills needed for effective LLM data preprocessing, responsible AI, security measures, model optimization, monitoring post-deployment, and cloud deployment.
2. Learning Objectives and Outcomes:
Data Preprocessing for LLM Training:
Responsible AI and GenAI:
Security Perspectives in GenAI:
Model Optimization Techniques for GenAI Models:
