AWS Entity Resolution launches support for incremental Machine Learning based matching workflows

AWS Entity Resolution launches support for Machine Learning (ML) based Incremental Matching workflows in General Availability, fundamentally transforming how enterprises process entity resolution at scale. Previously, adding even a single new record required customers to reprocess their entire dataset—a process that could take up to 2 days and cost thousands of dollars. This created a critical bottleneck that forced major businesses to seek costly workarounds or alternative solutions. 

With this enhancement, AWS Entity Resolution enables businesses to process only the new records added since their last workflow run. This launch provides dramatic efficiency gains: processing 1M incremental records in less than 1 hour which is a 95% reduction in processing time compared to current workloads , while also significantly reducing infrastructure costs. The feature supports incremental workloads up to 50M incremental records over datasets containing up to 1 billion historical base records, making AWS Entity Resolution viable for continuous, large-scale enterprise workloads that were previously economically unfeasible.

You can start using incremental ML workflows in all AWS Regions where AWS Entity Resolution is available. For more information on starting an incremental ML workflow, see our user guide. For more information about AWS Entity Resolution, visit our product page. 

Categories: marketing:marchitecture/contact-center,general:products/aws-entity-resolution,marketing:marchitecture/business-productivity

Source: Amazon Web Services

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