Advanced Techniques in LLM & Its Deployment Course Overview

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.

Target Audience for Advanced Techniques in LLM & Its Deployment

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.

• Machine Learning Engineers
• Data Scientists
• AI Researchers
• Data Engineers
• AI Solution Architects
• Software Developers focusing on AI/ML
• IT Security Specialists
• DevOps Engineers
• Product Managers in AI/ML fields
• Cloud Infrastructure Engineers
• AI Ethics Specialists
• IT Managers/CTOs

Learning Objectives - What you will Learn in this Advanced Techniques in LLM & Its Deployment?

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:

  • Understand and implement tokenization to break text into manageable units for model input.
  • Conduct text embedding to map text to continuous vectors for better semantic understanding.
  • Apply image embedding techniques to represent visual data as vectors.

• Responsible AI and GenAI:

  • Learn methods to mitigate bias in models to ensure fairness.
  • Gain insights into making AI decision-making processes transparent.
  • Establish frameworks for ensuring accountability in AI systems.

• Security Perspectives in GenAI:

  • Identify unique security risks specific to AI systems.
  • Implement strategies such as robustness training and privacy protection to safeguard AI models.

• Model Optimization Techniques for GenAI Models:

  • Apply pruning techniques to remove unnecessary parameters and reduce model size.
  • Utilize quantization to reduce numerical precision for faster inference.
  • Employ compression techniques like weight sharing to shrink model size