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

2026-05-17 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, quick

Summary

The discussion addresses the common challenge of insufficient or unsuitable public datasets for machine learning projects, where generic, outdated, or low-volume data fails to meet specific use case requirements. Traditional workarounds include accepting degraded performance, extensive manual data scraping and cleaning, or marginal improvements from augmentation techniques like SMOTE. One proposed solution involves sourcing permissively licensed real-world data, curating it to a company's specific schema, and then employing synthetic expansion to achieve the necessary volume and edge case coverage. This approach includes a fidelity report to ensure statistical alignment between synthetic and source data distributions, aiming to mitigate the "data wall" faced by many development teams.

Key takeaway

For ML engineers facing a "data wall" due to unsuitable public datasets, consider exploring solutions that combine curated real-world data with synthetic expansion. This approach can provide the necessary volume and domain specificity, potentially avoiding the time-consuming process of manual scraping or the performance compromises of generic data. Evaluate tools that offer fidelity reports to ensure synthetic data aligns with your target distribution.

Key insights

Public datasets often fail to meet specific ML project needs, necessitating alternative data sourcing and generation strategies.

Principles

• Domain specificity is crucial for model generalization.
• Statistical alignment ensures synthetic data fidelity.

Method

A proposed method involves curating permissively licensed real-world data to a specific schema, followed by synthetic expansion to meet volume and edge case requirements, validated by fidelity reports.

In practice

• Consider domain adaptation for simulation-to-real transfer.
• Explore synthetic data generation for volume and edge cases.

Topics

• Training Data
• Public Datasets
• Data Augmentation
• Synthetic Data Generation
• Domain Adaptation

Best for: NLP Engineer, Computer Vision Engineer, Machine Learning Engineer, Data Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.