An introduction to semantic model-driven AI, exploring how SHACL (Shape Constraint Language) can improve the reliability of LLM responses by providing structure and constraints to data.
This article explains the importance of data validation in a machine learning pipeline and demonstrates how to use TensorFlow Data Validation (TFDV) to validate data. It covers the 5 stages of machine learning validation: generating statistics from training data, inferring schema from training data, generating statistics for evaluation data and comparing it with training data, identifying and fixing anomalies, and checking for drifts and data skew.
