Best Practices for Building AI Datasets
summary
Starts with people doing the work not models so production launches use human-in-the-loop labeling, gradually replacing humans as data grows. Prioritize a dedicated dataset team over outsourcing, build a custom annotator to cut clicks and double throughput, and apply double-verification (two labelers plus tiebreaker) to avoid error amplification. Aim for orders of magnitude more samples where possible (100k vs 10k), include production data, expect relabeling, and accept noisy labels when methods tolerate them.
AI-written summary · about
chapters (15)
- 0:00Introduction
- 1:56Big Data
- 3:31Data Availability
- 5:18The Magic Algorithm
- 6:12Building AI from Scratch
- 6:57Start with Humans
- 8:33Chatbots
- 11:06Dedicated Data Set Team
- 15:04Outsourcing Data
- 19:06Noised Data
- 20:36Build Your Own Data Tool
- 24:24Change Data Labels
- 26:50Double Verify Data
- 30:39Focus on the Exceptions
- 34:53Always Include Production Data
related talks
40mData labelling to scale your ML and AI pipelines
33mKeys to Data-Centric AI: Asking AI to Improve its own Dataset.
30mData-Centric AI Cookbook-let's prep that data
36mApplied Machine Learning Playbook: From Idea to Production
08mAgents and Robots: The Human Effort Putting AI Into Production
30mActive Learning and Human-in-the-Loop Machine Learning
41mModels Schm-odels: Why you Should Care about Data-Centric AI
08mConsiderations for producing the best AI and Machine Learning models