Prepare training datasets

About this Video

• Video Title: Prepare training datasets
• Channel: Gooru Content
• Speakers: The transcript does not name the speaker.
• Duration: 00:06:03

Introduction

This video discusses the crucial process of preparing training datasets for fine-tuning foundation models in AI. It emphasizes the importance of high-quality, balanced data free from harmful biases to build successful, ethical, and high-performing AI applications. The video outlines a structured approach involving strategic data collection, bias mitigation, rigorous quality control, and iterative validation.

Key Takeaways

• Strategic Data Collection: The process begins with gathering data from diverse sources, including internal data (logs, documentation), external data (public datasets, articles), and synthetic data generated by AI to fill gaps and increase diversity. Data preparation involves cleaning, formatting, and splitting into training, validation, and test sets.
• Bias Mitigation: The video highlights several types of bias (selection, reporting, automation, historical) and emphasizes the importance of identifying and mitigating them. GenAI tools can assist in bias detection and mitigation through bias auditing, fairness metric computation, counterfactual data generation, and content rephrasing. Human review remains crucial.
• Quality Control and Annotation: Clear annotation guidelines are essential for human annotators. GenAI can aid in quality checks by providing initial labels or flagging inconsistencies. Inter-annotator agreement measurements gauge reliability.
• Iterative Process: Data creation, curation, and fine-tuning are iterative processes requiring continuous refinement and validation.