Data Labeling Best Practices for Fine-Tuning LLMs
Offered By: MLOps.community via YouTube
Course Description
Overview
Discover essential data labeling best practices in this lightning talk from the AI in Production Conference. Learn how to optimize your approach to fine-tuning open-source LLMs, covering key aspects such as hiring data labelers, preparing datasets, and effectively managing your labeling team. Gain insights from Charles Brecque, founder and CEO of TextMine, as he shares his expertise in leveraging knowledge graph technology and large language models for structuring unstructured data in documents. Explore strategies to enhance your LLM's performance and avoid common pitfalls in data labeling. This 13-minute presentation offers valuable knowledge for professionals working with AI and machine learning, particularly those involved in document processing and natural language understanding.
Syllabus
Data Labeling Best Practices // Charles Brecque // AI in Production Conference Lightning Talk
Taught by
MLOps.community
Related Courses
Machine Learning Operations (MLOps): Getting StartedGoogle Cloud via Coursera Проектирование и реализация систем машинного обучения
Higher School of Economics via Coursera Demystifying Machine Learning Operations (MLOps)
Pluralsight Machine Learning Engineer with Microsoft Azure
Microsoft via Udacity Machine Learning Engineering for Production (MLOps)
DeepLearning.AI via Coursera