Amazon Technologies, Inc.

Nikhil Anand Navali

Archiman Dutta

Xianshun Chen

Kai Chieh Liu

Zahed Patel

Kai Liu

Multiple distinct control descriptors, each specifying an algorithm and values of one or more parameters of the algorithm, are created. A plurality of tuples, each indicating a respective record of a data set and a respective descriptor, are generated. The tuples are distributed among a plurality of compute resources such that the number of distinct descriptors indicated in the tuples received at a given resource is below a threshold. The algorithm is executed in accordance with the descriptors' parameters at individual compute resources.

Artificial intelligence system providing automated distributed training of machine learning models

A hierarchical representation of an input data set comprising similarity scores for respective entity pairs is generated iteratively. In a particular iteration, clusters are obtained from a subset of the iteration's input entity pairs which satisfy a similarity criterion, and then spanning trees are generated for at least some of the clusters. An indication of at least a representative pair of one or more of the clusters is added to the hierarchical representation in the iteration. The hierarchical representation is used to respond to clustering requests.

Scalable hierarchical clustering

Entity record pairs are extracted from a selected cluster of entity records. Attribute value pairs are obtained from the entity record pairs. Labels are assigned to the attribute value pairs based at least in part on entity-level similarity scores of the entity records from which the attribute value pairs were obtained. A machine learning model is trained, using a data set which includes at least some attribute value pairs to which the labels are assigned, to generate attribute similarity scores for pairs of attribute values.

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Seattle, WA, United States of America

Nikhil Anand Navali