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Fine-Tuning Large Language Models: Maximizing Value and Performance for Custom AI Solutions

In this 2-day course, participants will learn how to fine-tune large language models like Chat-GPT to build custom AI solutions tailored to specific use cases and domains. The course will cover the...

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$2,000 USD
Duration 2 days
Available Formats Classroom

In this 2-day course, participants will learn how to fine-tune large language models like Chat-GPT to build custom AI solutions tailored to specific use cases and domains. The course will cover the essentials of fine-tuning, including data preparation, model selection, and training best practices. Participants will also learn how to evaluate and optimize fine-tuned models for improved performance, fairness, and safety. The course will provide hands-on experience through guided exercises and real-world examples, highlighting various use cases such as content generation, sentiment analysis, and customer service. By the end of the course, participants will be equipped with the skills and knowledge required to develop high-performing, customized AI solutions that deliver tangible value to their organizations.

Skills Gained

  • Understand the principles and benefits of fine-tuning large language models like Chat-GPT
  • Prepare data sets and choose appropriate models for fine-tuning tasks
  • Implement best practices for training and optimizing fine-tuned models
  • Evaluate model performance, fairness, and safety in custom AI applications
  • Apply fine-tuning techniques to create AI solutions for various use cases and domain

Who Can Benefit

Data scientists, AI/ML engineers, software developers, and professionals interested in developing custom AI applications using large language models like Chat-GPT


  • Strong understanding of AI and machine learning concepts
  • Familiarity with natural language processing (NLP) techniques and tools
  • Experience in Python programming and working knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch)

Course Details

Throughout the course, participants will engage in hands-on exercises and case studies to reinforce learning and facilitate the practical application of fine-tuning techniques. Group discussions will encourage collaboration and knowledge sharing among peers. The capstone project at the end of the course will allow participants to demonstrate their fine-tuning skills by developing a custom AI solution that addresses a real-world challenge or opportunity using a large language model like Chat-GPT.

By focusing on the value and use cases of fine-tuned large language models, this course will empower participants to harness the potential of state-of-the-art AI technology for a wide range of applications. Participants will leave the course with a deep understanding of the fine-tuning process and the expertise to create AI solutions tailored to their organization's needs, delivering enhanced performance, increased efficiency, and competitive advantage.

Day 1

Module 1: Introduction to Large Language Models and Fine-Tuning

  • Overview of large language models (e.g., GPT-3, Chat-GPT)
  • Benefits and challenges of fine-tuning
  • Introduction to fine-tuning techniques and tools

Module 2: Data Preparation and Model Selection

  • Principles of data selection and annotation for fine-tuning
  • Techniques for data preprocessing and cleaning
  • Criteria for selecting base models and architectures
  • Hands-on exercise: Preparing data sets and selecting models for custom AI applications

Module 3: Training and Optimizing Fine-Tuned Models

  • Best practices for training and hyperparameter tuning
  • Techniques for model optimization and regularization
  • Monitoring model convergence and addressing overfitting
  • Hands-on exercise: Training and optimizing a fine-tuned model for a specific use case

Day 2

Module 4: Evaluating Model Performance, Fairness, and Safety

  • Metrics and techniques for model evaluation
  • Identifying and mitigating biases in fine-tuned models
  • Ensuring content safety and adherence to ethical guidelines
  • Hands-on exercise: Evaluating and improving fine-tuned model performance, fairness, and safety

Module 5: Fine-Tuning for Various Use Cases and Domains

  • Customizing AI solutions for content generation, sentiment analysis, customer service, and more
  • Adapting fine-tuning techniques for domain-specific applications
  • Hands-on exercise: Fine-tuning a model for a specific use case and domain

Module 6: Capstone Project

  • Participants will apply the concepts and techniques learned throughout the course to fine-tune a large language model for a custom AI solution addressing a real-world challenge or opportunity
  • Presentation and discussion of capstone projects