How are you handling training data when public datasets don’t match your use case? [D]

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• The post discusses issues with publicly available datasets, which are often too generic or not aligned with specific use cases.
• It highlights common approaches like accepting degraded performance, spending time cleaning and scraping data, or using augmentation techniques. The author introduces their method of sourcing permissively licensed real-world data, curating it to fit a company’s needs, and augmenting as necessary.

– This post seeks feedback on whether the described approach is common or if other teams have found more effective workarounds.
– The author encourages others facing similar challenges to share their experiences.
– Approaches like using real-world data, curating it to fit specific needs, and augmenting as needed are presented as potential solutions to overcoming data limitations in AI projects.

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Originally published at reddit.com. Curated by AI Maestro.

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