AI systems rely heavily on the quality and structure of the data used to train them. This detailed article from Amplework explains why proper data modelling is essential before any machine learning process begins. It covers how raw data is assessed, cleaned, validated, formatted, and converted into meaningful structures. The blog also highlights the importance of schema design, feature engineering, and dataset splitting—key steps that help avoid biased outputs and poor performance in AI models. Whether you're a beginner or expert, this guide will help you understand how structured data forms the backbone of reliable AI training.
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