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Practical AI
Towards high-quality (maybe synthetic) datasets
Oct 9, 2024
David Berenstein, a developer advocate engineer at Hugging Face, and Ben Burtenshaw, a machine learning engineer at Argilla, dive into the crucial realm of data quality in AI. They discuss how collaboration between domain experts and data scientists significantly enhances model efficacy. The conversation covers innovative strategies for generating synthetic datasets, utilizing AI for labeling, and maintaining privacy. The duo also shares insights on the importance of effective feedback loops and multimodal data integration for refining AI training.
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Quick takeaways
- Effective data collaboration between domain experts and data scientists is essential for achieving successful AI outcomes and reliable models.
- Organizations must model their problems clearly to generate relevant labeled datasets, enhancing the alignment of their data landscape with AI workflows.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode