At a machine learning service, a determination is made that an analysis to detect whether at least a portion of contents of one or more observation records of a first data set are duplicated in a second set of observation records is to be performed. A duplication metric is obtained, indicative of a non-zero probability that one or more observation records of the second set are duplicates of respective observation records of the first set. In response to determining that the duplication metric meets a threshold criterion, one or more responsive actions are initiated, such as the transmission of a notification to a client of the service.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: one or more computing devices configured to: generate, at a machine learning service of a provider network, one or more space-efficient representations of a first set of observation records associated with a machine learning model, wherein individual ones of the space-efficient representations utilize less storage than the first set of observation records, and wherein at least a subset of observation records of the first set include respective values of a first group of one or more variables; receive an indication that a second set of observation records is to be examined for the presence of duplicates of observation records of the first set in accordance with a probabilistic duplicate detection technique, wherein at least a subset of observation records of the second set include respective values of the first group of one or more variables; obtain, using at least one space-efficient representation of the one or more space-efficient representations, a duplication metric corresponding to at least a portion of the second set, indicative of a non-zero probability that one or more observation records of the second set are duplicates of one or more observation records of the first set with respect to at least the first group of one or more variables; and in response to a determination that the duplication metric meets a threshold criterion, implement one or more responsive actions including a notification of a detection of potential duplicate observation records to the client. 2. The system as recited in claim 1, wherein a particular space-efficient representation of the one or more space-efficient representations includes one or more of: (a) a Bloom filter, (b) a quotient filter, or (c) a skip list. 3. The system as recited in claim 1, wherein the first set of one or more observation records comprises a training data set of the machine learning model, and wherein the second set of one or more observation records comprises a test data set of the machine learning model. 4. The system as recited in claim 1, wherein a particular space-efficient representation of the one or more space-efficient representations includes a Bloom filter, wherein the one or more computing devices are further configured to: estimate, prior to generating the Bloom filter, (a) an approximate count of observation records included in the first set and (b) an approximate size of individual observation records of the first set; and determine, based at least in part on the approximate count or the approximate size, one or more parameters to be used to generate the Bloom filter, including one or more of: (a) a number of bits to be included in the Bloom filter (b) a number of hash functions to be used to generate the Bloom filter, or (c) a particular type of hash function to be used to generate the Bloom filter. 5. The system as recited in claim 1, wherein the one or more responsive actions include one or more of: (a) a transmission of an indication, to the client, of a particular observation record of the second set which has been identified as having a non-zero probability of being a duplicate, (b) a removal, from the second set, of a particular observation record which has been identified as having a non-zero probability of being a duplicate, prior to performing a particular machine learning task using the second set, (c) a transmission, to the client, of an indication of a potential prediction error associated with removing, from the second set, one or more observation records which have been identified as having non-zero probabilities of being duplicates, or (d) a cancellation of a machine learning job associated with the second set. 6. A method, comprising: performing, by one or more computing devices: generating, at a machine learning service, one or more alternate representations of a first set of observation records, wherein at least one alternate representation occupies a different amount of space than the first set of observation records; obtaining, using at least one alternate representation of the one or more alternate representations, a duplication metric corresponding to at least a portion of a second set of observation records, indicative of a non-zero probability that one or more observation records of the second set are duplicates of respective observation records of the first set, with respect to one or more variables for which respective values are included in at least some observation records of the first set; and in response to determining that the duplication metric meets a threshold criterion, implementing one or more responsive actions. 7. The method as recited in claim 6, wherein a particular alternate representation of the one or more alternate representations includes one or more of: (a) a Bloom filter, (b) a quotient filter, or (c) a skip list. 8. The method as recited in claim 6, wherein the first set of one or more observation records comprises a training data set of a particular machine learning model, and wherein the second set of one or more observation records comprises a test data set of the particular machine learning model. 9. The method as recited in claim 6, wherein a particular alternate representation of the one or more alternate representations includes a Bloom filter, further comprising performing, by the one or more computing devices: estimating, prior to generating the Bloom filter, (a) an approximate count of observation records included in the first set and (b) an approximate size of individual observation records of the first set; and determining, based at least in part on the approximate count or the approximate size, one or more parameters to be used to generate the Bloom filter, including one or more of: (a) a number of bits to be included in the Bloom filter (b) a number of hash functions to be used to generate the Bloom filter, or (c) a particular type of hash function to be used to generate the Bloom filter. 10. The method as recited in claim 6, wherein the one or more response actions include one or more of: (a) notifying a client of a detection of potential duplicate observation records, (b) providing an indication of a particular observation record of the second set which has been identified as having a non-zero probability of being a duplicate, (c) removing, from the second set, a particular observation record which has been identified as having a non-zero probability of being a duplicate, prior to performing a particular machine learning task using the second set, (d) providing, to a client, an indication of a potential prediction error associated with removing, from the second data set, one or more observation records which have been identified as having non-zero probabilities of being duplicates, or (e) abandoning a machine learning job associated with the second set. 11. The method as recited in claim 6, wherein a particular responsive action of the one or more responsive actions comprises providing an indication of a confidence level that a particular observation record of the second set is a duplicate. 12. The method as recited in claim 6, wherein the group of one or more variables excludes an output variable whose value is to be predicted by a machine learning model. 13. The method as recited in claim 6, wherein said determining that the duplication metric meets a threshold criterion comprises one or more of: (a) determining that the number of observation records of the second set which have been identified as having non-zero probabilities of being duplicates exceeds a first threshold or (b) determining that the fraction of the observation records of the second set that have been identified as having non-zero probabilities of being duplicates exceeds a second threshold. 14. The method as recited in claim 6, wherein said generating the one or more alternate representations of the first set of observation records comprises: subdividing the first set of observation records into a plurality of partitions; generating, at respective servers of the machine learning service, a respective Bloom filter corresponding to individual ones of the plurality of partitions; and combining the Bloom filters generated at the respective servers into a consolidated Bloom filter. 15. The method as recited in claim 6, further comprising performing, by the one or more computing devices: receiving, via a programmatic interface, an indication from the client of one or more of (a) a parameter to be used by the machine learning service to determine whether the threshold criterion has been met, or (b) the one or more responsive actions. 16. The method as recited in claim 6, wherein the first set of observation records and the second set of observation records are respective subsets of one of: (a) a training data set of a particular machine learning model, (b) a test data set of a particular machine learning model, or (c) a source data set from which a training data set of a particular machine learning model and a test data set of the particular machine learning model are to be obtained. 17. A non-transitory computer-accessible storage medium storing program instructions that when executed on one or more processors: determine, at a machine learning service, that an analysis to detect whether at least a portion of contents of one or more observation records of a first set of observation records are duplicated in a second set of observation records is to be performed; obtain a duplication metric corresponding to at least a portion of a second set of observation records, indicative of a non-zero probability that one or more observation records of the second set are duplicates of respective observation records of the first set, with respect to one or more variables for which respective values are included in at least some observation records of the first set; and in response to a determination that the duplication metric meets a threshold criterion, implement one or more responsive actions. 18. The non-transitory computer-accessible storage medium as recited in claim 17, wherein to obtain the alternate metric, the instructions when executed on the one or more processors generate an alternate representation of the first set of observation records, wherein the alternate representation includes one or more of: (a) a Bloom filter, (b) a quotient filter, or (c) a skip list. 19. The non-transitory computer-accessible storage medium as recited in claim 17, wherein the first set of one or more observation records comprises a training data set of a particular machine learning model, and wherein the second set of one or more observation records comprises a test data set of the particular machine learning model. 20. The non-transitory computer-accessible storage medium as recited in claim 17, wherein a particular responsive action of the one or more responsive actions comprises providing an indication of a confidence level that a particular observation record of the second set is a duplicate.