Data validation is the cheapest point in the pipeline to catch assumptions that would otherwise become model failures.
Validate schema and ranges
Check types, required fields, categories, units, and expected ranges before training or inference.
Audit missingness and imbalance
Understand whether missing values carry meaning and whether class distribution can distort the chosen metrics or training process.
Detect leakage
Make the prediction-time boundary explicit so a feature cannot accidentally contain information that would only exist after the prediction.
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AI Developer / ML Engineer building end-to-end AI systems from research to production, with a focus on multimodal AI, LLM applications, retrieval, MLOps, and systems engineering. He is based in Rawalpindi, Pakistan and is the founder of GROVE SYSTEMS.