Abstract: A method of optimizing training of a machine learning model is disclosed. The method may include receiving an input training data using an optimization device. The input training data may include a plurality of training data samples. Further, a set of relevant training data samples from the input training data may be identified. The method may use the optimization device to select a suitable type and configuration of a machine learning model from a plurality of types and configurations of machine learning models for processing the set of relevant training data samples. Furthermore, the method may validate the set of relevant training data samples.

Abstract: A method of selecting samples to represent a cluster is disclosed. The method may include receiving one or more clusters by an optimization device. Each of the one or more clusters may include a plurality of samples. The method may determine a count of number of samples to be selected from each of the one or more clusters and may generate an array-based distance matrix for each of the one or more clusters. The method may sort the plurality of samples of the cluster based on a degree of variability of the plurality of samples in the cluster. The sorting may be performed using the array-based distance matrix for each of the one or more clusters. Further, the method may select the determined count of number of samples from the sorted plurality of samples of each of the plurality of clusters to represent the cluster.

Abstract: Method (400) and system for clustering data samples is disclosed. The method (400) may include receiving (402) a plurality of batches, each of the plurality of batches including a plurality of samples, and creating (404) a set of clusters from a first batch of the plurality of batches, using a clustering technique. Each cluster of the set of clusters may include one or more samples. The one or more samples are determined based on variability of the one or more samples and cluster attributes associated with each cluster. The method may further include reconfiguring (406) the set of clusters that includes at least one of: repopulating (400A) the existing set of clusters with samples from remaining batches of the plurality of batches; and adding (400B) a new set of clusters to the existing set of clusters and populating the new set of clusters with samples from the remaining batches.

Abstract: A search engine provides customized search results in response to a search query using a group search ranker. The customized search results may be determined for a user based on the user's association with a user group having common preferences. Preferences for users may be learned through historical user information that includes interactions by users with websites on the Internet. Users may be grouped based on these preferences, and the user group may have an associated group search ranker. When the user enters a search query at a search engine, the search engine may identify a user group and retrieve the group search ranker. The group search ranker may be used to rank relevant search results identified based on the search query. The ranked search results may be provided for presentation to a user at a user computing device.

Abstract: A search engine provides customized search results in response to a search query using a group search ranker. The customized search results may be determined for a user based on the user's association with a user group having common preferences. Preferences for users may be learned through historical user information that includes interactions by users with websites on the Internet. Users may be grouped based on these preferences, and the user group may have an associated group search ranker. When the user enters a search query at a search engine, the search engine may identify a user group and retrieve the group search ranker. The group search ranker may be used to rank relevant search results identified based on the search query. The ranked search results may be provided for presentation to a user at a user computing device.