Preparing training datasets

About this Video

• Video Title: Preparing training datasets
• Channel: Gooru Content
• Speakers: None specified in transcript
• Duration: 00:04:33

Introduction

This video details the crucial process of preparing datasets for Generative AI fine-tuning. It emphasizes the importance of data quality, bias mitigation, and robust technical processes for creating successful and ethical AI models. The video explains that preparing training data is not simply "feeding data to a model," but rather crafting the right data for optimal performance.

Key Takeaways

• Data Quality is Paramount: The quality of the fine-tuning dataset directly impacts the model's performance, generalization ability, and bias. "Garbage in, garbage out" applies significantly here.
• Data Sourcing and Transformation: Sourcing relevant data from various sources, handling different data formats, and adhering to compliance requirements are crucial. Transforming raw data involves handling missing values, reducing noise, standardizing features, and masking PII.
• Ground Truth Creation: This involves a well-defined annotation process, clear guidelines for annotators, and rigorous quality control (including inter-annotator agreement metrics) to ensure labeling consistency.
• Data Preparation and Splitting: Preparing data for the model includes cleaning, formatting, and structuring it for efficient loading. Creating reliable evaluation sets (training, validation, and testing) is vital to prevent data leakage and ensure unbiased evaluation. Stratification ensures similar class proportions across all splits.
• Bias Detection and Mitigation: Proactively detecting and mitigating bias (from historical data collection methods, labeling subjectivity, etc.) is crucial. This involves employing detection methods, analyzing demographic attributes, measuring performance disparities, and using mitigation strategies (sampling, data augmentation, careful annotation guidelines).