Walmart Apollo, LLC

Gajendra Alias Nishad Kamat

Mridul Jain

Saigopal Thota

Xun Luan

Saurabh Agrawal

Pawan Kumar Gupta

Giridhar Addepalli

Santos Kumar Das

Albin Kuruvilla

Navinder Pal Singh Brar

Antriksh Akshesh Shah

Saumya Agarwal

Neil Mohan Reddy Palleti

Rijul Magu

Ashraful Arefeen

Pragya Jain

Mitesh Sinha

Sai Kiran Reddy Malikireddy

Sébastien Jean-Maurice Olivier Péhu

Rewati Mahendra Ovalekar

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform ingesting streaming events for processing by multiple models; mapping each of the streaming events to a model of the multiple models; storing each of the streaming events in a respective queue in a respective sequence store, such that a respective one of the multiple models retrieves (i) a respective one of the streaming events in the respective sequence store associated with the respective one of the multiple models and (ii) a respective key corresponding to the respective one of the streaming events from a leaf store, to asynchronously perform the respective machine-learning inferencing, wherein the multiple models run independently and in parallel on multi-tenant threads. Other embodiments are disclosed.

Asynchronous multi-tenant model inferencing on streaming databases

This application relates to apparatus and methods for automatically associating customer data to a corresponding customer. A computing device may receive linking data identifying a plurality of links, where each like associates at least two nodes that each represent customer data. The computing device may partition the linking data into multiple partitions, and cause a union find algorithm to be executed for each partition in parallel to associate each node with a parent ID. The computing device may iteratively execute a global shuffle algorithm to place all same nodes in a same partition, and may assign a same parent ID to the same nodes. The computing device may iteratively execute a path compression algorithm across all partitions to generate a graph output that associates all child nodes of a same parent node with the same parent ID.

Method and apparatus for determining associated data linkages

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: generating linkage scores between nodes at least based on a machine learning model; creating links between the nodes to form connected components based on the linkage scores exceeding a predetermined threshold; generating an actual matching linkage set of the nodes linked in the connected components by using a relaxed blocking criteria; and generating a quality score for the connected components. Other embodiments are disclosed.

Automatically measuring quality scores for connected components using machine learning models

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: bundling multiple registered queries of a dataset using a scheduling technique, wherein the dataset is homogenous in schema; running a single table scan of the dataset to process the multiple registered queries of the dataset in parallel; and generating a respective output responsive to each of the multiple registered queries. Other embodiments are disclosed.

Optimizing scans using query planning on batch data

This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.

Method and apparatus for determining data linkage confidence levels

Systems and methods of real-time modeling pipeline inferencing are disclosed. At least one model configured to calculate at least one metric from one or more features is deployed. A model inferencing pipeline configured to extract the one or more features from a customer-specific data pipeline is implemented for the at least one mode. The model inferencing pipeline is generated using a training data set extracted from a cross-customer data pipeline. The at least one metric is calculated using the one or more features extracted from the customer-specific data pipeline.

System and method for real-time modeling inference pipeline

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Cupertino, CA, United States of America

Gajendra Alias Nishad Kamat