[MC 12] Intro Fine Tuning and Data Preparation This masterclass is designed to provide participants with a solid foundation in fine-tuning AI models and preparing data effectively to achieve optimal performance. As part of the AI Residency program, this session will guide attendees through the essential concepts, techniques, and best practices required to adapt pre-trained models to specific use cases. What You’ll Learn: Understanding Fine-Tuning: The importance of fine-tuning pre-trained models for domain-specific applications. Key differences between fine-tuning and traditional model training. When and why to fine-tune a model versus using it off-the-shelf. Data Preparation Fundamentals: Collecting and curating high-quality datasets for fine-tuning. Data cleaning techniques to remove noise and inconsistencies. Structuring and formatting data for compatibility with fine-tuning frameworks. Ethical considerations and bias mitigation in dataset preparation. Fine-Tuning Techniques: Selecting the right pre-trained models for specific tasks. Hyperparameter tuning strategies for improved performance. Evaluating and optimizing fine-tuned models. Tools and Frameworks: Hands-on experience with popular libraries such as Hugging Face, TensorFlow, and PyTorch. Leveraging cloud resources for scalable fine-tuning. Who Should Attend: This masterclass is ideal for AI practitioners, data scientists, and developers looking to deepen their understanding of fine-tuning and data preparation to build more accurate and efficient AI applications. Prerequisites: A basic understanding of machine learning concepts and familiarity with Python programming will be beneficial for participants to make the most out of this session. Join us in this session to master the critical skills required to fine-tune AI models effectively and prepare data that drives meaningful insights and performance.