AWS Entity Resolution adds record-level confidence scores for ML matching
AWS Entity Resolution now provides record-level confidence scores for ML-based matching workflows, enabling customers to qualify more records for activation by applying differentiated thresholds.
AWS Entity Resolution now provides machine learning-based matching workflows with record-level confidence scores, assigning each record a score that reflects actual match quality rather than a uniform group-level score. This allows higher confidence for automatic merging and lower thresholds for additional qualifying records, enabling more records to be activated and improving target audience reach and lead conversion. It also maintains compliance-level match transparency by providing auditable evidence for each record. In incremental ML workflows, the existing RecordConfidenceLevel column reflects per-record scores without requiring schema changes, and AWS Entity Resolution is available in all AWS regions.
Why it matters
AWS Entity Resolution is a service that helps unify customer data and eliminate duplicates. This update allows for more detailed evaluation of machine learning-based matching accuracy, improving the precision and efficiency of data integration.