Vertex AI provides explanation tools that help you understand model predictions and validate that models use sensible signals. For tabular and image models trained with supported frameworks or AutoML, you can enable feature attributions to get per-feature contribution scores for each prediction. Methods such as integrated gradients or sample-based perturbations quantify how input changes affect outputs, and the results can be surfaced in notebooks, the console, or exported for reports. You can attach these attributions to batch predictions as well, making them available for audits.
Beyond raw attributions, Vertex AI integrates with evaluation workflows so you can slice metrics across cohorts and detect fairness or performance issues. You can define evaluation datasets, compute confusion matrices and precision/recall by segment, and compare candidate models before promotion. Model Monitoring extends this by watching production traffic for distribution shifts and alerting when inputs drift from the training baseline—an early indicator that explanations may no longer hold or that performance could degrade.
In retrieval-augmented systems, explanations are often about why a document was retrieved. While vector similarity is numeric, you can log nearest-neighbor distances, highlight matched passages, and store metadata (e.g., section titles) to improve human interpretability. Combine Milvus’s top-k results with lightweight re-ranking to produce stable, understandable orders, and record which context snippets were provided to the generator endpoint. This creates traceable, reviewable chains from user query → embeddings → neighbors → final answer, which is crucial when explaining outcomes to stakeholders.