Greedy inference for resource-efficient matching of entities

1 . A computer-implemented method for matching entities using a machine learning (ML) model, the method being executed by one or more processors and comprising:

during a pre-deployment phase:

determining a set of potential probability thresholds based on a set of inference results provided by processing testing data through the ML model, each inference result being associated with a probability indicating a confidence that the respective inference result is correct, each probability being included in the set of potential probability thresholds;

for each potential probability threshold in the set of potential probability thresholds, determining an accuracy as a number of correct values predicted at or above the respective potential probability threshold among all values predicted at or above the respective potential probability threshold; and

selecting a probability threshold from the set of potential probability thresholds;

deploying the ML model for inference in a deployment phase, the ML model being deployed with the probability threshold to reduce a number of entities from inference jobs processed by the ML model; and

during the deployment phase:

processing an inference job comprising a first set of entity pairs through the ML model to assign a label to each entity pair in the set entity pairs, each label being associated with a probability and comprising a type of multiple types; and

for each entity pair having a label of one or more specified types, selectively removing an entity of the entity pair from further processing of the inference job by the ML model based on whether the probability associated with the label meets or exceeds the probability threshold;

wherein selectively removing an entity of the entity pair from further processing of the inference job by the ML model comprises adding a key of the entity to a set of matched keys in response to determining that the probability associated with the label, wherein the set of matched keys is used to selectively filter entities from being processed in the inference job.

2 . The method of claim 1 , wherein the probability threshold is selected as a lowest potential probability threshold in the set of potential probability thresholds having an accuracy that meets or exceeds a target accuracy.

3 . The method of claim 1 , wherein the one or more specified types comprise one or more of a single match and a multi-match.

4 . The method of claim 1 , wherein the set of potential probability thresholds comprises unique probabilities included in the inference results.

5 . The method of claim 1 , further comprising:

determining a set of keys for a set of entities, each key in the set of keys uniquely identifying an entity;

comparing keys in the set of keys to matched keys in a set of matched keys; and

removing an entity from the set of entities in response to determining that a key identifying the entity is included in the set of matched keys.

6 . The method of claim 1 , wherein each entity pair comprises a query entity and a target entity, the target entity being selectively removed based on whether the probability associated with the label meets or exceeds the probability threshold.

7 . A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for matching entities using a machine learning (ML) model, the operations comprising:

during a pre-deployment phase:

determining a set of potential probability thresholds based on a set of inference results provided by processing testing data through the ML model, each inference result being associated with a probability indicating a confidence that the respective inference result is correct, each probability being included in the set of potential probability thresholds;

for each potential probability threshold in the set of potential probability thresholds, determining an accuracy as a number of correct values predicted at or above the respective potential probability threshold among all values predicted at or above the respective potential probability threshold; and

selecting a probability threshold from the set of potential probability thresholds;

deploying the ML model for inference in a deployment phase, the ML model being deployed with the probability threshold to reduce a number of entities from inference jobs processed by the ML model; and

during the deployment phase:

processing an inference job comprising a first set of entity pairs through the ML model to assign a label to each entity pair in the set entity pairs, each label being associated with a probability and comprising a type of multiple types; and

for each entity pair having a label of one or more specified types, selectively removing an entity of the entity pair from further processing of the inference job by the ML model based on whether the probability associated with the label meets or exceeds the probability threshold,

wherein selectively removing an entity of the entity pair from further processing of the inference job by the ML model comprises adding a key of the entity to a set of matched keys in response to determining that the probability associated with the label, wherein the set of matched keys is used to selectively filter entities from being processed in the inference job.

8 . The non-transitory computer-readable storage medium of claim 7 , wherein the probability threshold is selected as a lowest potential probability threshold in the set of potential probability thresholds having an accuracy that meets or exceeds a target accuracy.

9 . The non-transitory computer-readable storage medium of claim 7 , wherein the one or more specified types comprise one or more of a single match and a multi-match.

10 . The non-transitory computer-readable storage medium of claim 7 , wherein the set of potential probability thresholds comprises unique probabilities included in the inference results.

11 . The non-transitory computer-readable storage medium of claim 7 , wherein operations further comprise:

determining a set of keys for a set of entities, each key in the set of keys uniquely identifying an entity;

comparing keys in the set of keys to matched keys in a set of matched keys; and

removing an entity from the set of entities in response to determining that a key identifying the entity is included in the set of matched keys.

12 . The non-transitory computer-readable storage medium of claim 7 , wherein each entity pair comprises a query entity and a target entity, the target entity being selectively removed based on whether the probability associated with the label meets or exceeds the probability threshold.

13 . A system, comprising:

a computing device; and

a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for matching entities using a machine learning (ML) model, the operations comprising:

during a pre-deployment phase:

determining a set of potential probability thresholds based on a set of inference results provided by processing testing data through the ML model, each inference result being associated with a probability indicating a confidence that the respective inference result is correct, each probability being included in the set of potential probability thresholds;

for each potential probability threshold in the set of potential probability thresholds, determining an accuracy as a number of correct values predicted at or above the respective potential probability threshold among all values predicted at or above the respective potential probability threshold; and

selecting a probability threshold from the set of potential probability thresholds;

deploying the ML model for inference in a deployment phase, the ML model being deployed with the probability threshold to reduce a number of entities from inference jobs processed by the ML model; and

during the deployment phase:

processing an inference job comprising a first set of entity pairs through the ML model to assign a label to each entity pair in the set entity pairs, each label being associated with a probability and comprising a type of multiple types; and

for each entity pair having a label of one or more specified types, selectively removing an entity of the entity pair from further processing of the inference job by the ML model based on whether the probability associated with the label meets or exceeds the probability threshold:

wherein selectively removing an entity of the entity pair from further processing of the inference job by the ML model comprises adding a key of the entity to a set of matched keys in response to determining that the probability associated with the label, wherein the set of matched keys is used to selectively filter entities from being processed in the inference job.

14 . The system of claim 13 , wherein the probability threshold is selected as a lowest potential probability threshold in the set of potential probability thresholds having an accuracy that meets or exceeds a target accuracy.

15 . The system of claim 13 , wherein the one or more specified types comprise one or more of a single match and a multi-match.

16 . The system of claim 13 , wherein the set of potential probability thresholds comprises unique probabilities included in the inference results.

17 . The system of claim 13 , wherein operations further comprise:

determining a set of keys for a set of entities, each key in the set of keys uniquely identifying an entity;

comparing keys in the set of keys to matched keys in a set of matched keys; and

removing an entity from the set of entities in response to determining that a key identifying the entity is included in the set of matched keys.