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DAWhy AI models fail even with perfect architecture 📊
Everyone talks about model architecture - more parameters, better benchmarks, new frameworks. But many AI projects struggle for a much simpler reason: poor data collection.
In this video, we highlight several mistakes teams keep repeating when preparing data for AI training. Things like relying on massive datasets instead of high-quality signals, ignoring rare events, overusing synthetic data, or accidentally introducing target leakage, which makes models look accurate during testing but fail in production.
We also touch on how unstable data pipelines, duplicates, outdated records, or missing documentation can quietly undermine even the best model architecture.
Good AI systems don’t come only from better models - they come from carefully collected and maintained data.
🎬 If you’d like to see the full breakdown and practical tips, watch the video👇🏼
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