Palantir Technologies Inc.

Paul Gribelyuk

Pierre Cholet

Kelvin Lau

Ankit Shankar

Thomas Powell

Myles Scolnick

Andre Frederico Cavalheiro Menck

Steven Fackler

Bianca Rahill-Marier

Casey Patton

Kayo Teramoto

Jim Inoue

William Seaton

Benjamin Lee

Andrew Poh

Maciej Albin

Jonah Scheinerman

Benjamin Grabham

Han Xu

Arion Sprague

Gregoire Omont

Aaron Rubin

Tam-Sanh Nguyen

David Lisuk

Maxim Kesin

Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.

Systems and methods for implementing a machine learning approach to modeling entity behavior

A data ingestion system normalizes ingested documents and extracts data based on a template that is applied to the documents. In an aspect, the system accesses a document of a document type and determines a template to apply to the document. The system normalizes the document, extracts data values from the document based at least in part on the template, and generates structured data based at least partly on the extracted data.

Data normalization and extraction system

Systems and methods are provided for identifying relationships between defects. The system may obtain defect items and associated information. Defect items may be compared to one another based on their attributes to determine how related they are. According to the comparisons, defect items may be grouped together into issue items for further analysis by a user. The system may further update a defect comparison model according to user interaction with defect items.

Systems and methods for determining relationships between defects

Techniques for configuring and validating a data pipeline system deployment are described. In an embodiment, a template is a file or data object that describes a package of related jobs. For example, a template may describe a set of jobs necessary for deduplication of data records or a set of jobs performing machine learning on a set of data records. The template can be defined in a file, such as a JSON blob or XML file. For each job specified in the template, the template may identify a set of dataset dependencies that are needed as input for the processing of that job. For each job specified in the template, the template may further identify a set of configuration parameters needed for deployment of the job. In an embodiment, a server uses the template and the configuration parameter values collected via the GUI to generate code for the package of jobs. The code may be stored in a version control system. In an embodiment, the code may be compiled, executed, and deployed to a server for processing the data.

Techniques for configuring and validating a data pipeline deployment

In various example embodiments, a textual identification system is configured to receive a set of search terms and identify a set of textual data based on the search terms. The textual identification system retrieves a data structure including textual identifications for the set of textual data and processes the data structure to generate a modified data structure. The textual identification system sums rows within the modified data structure and identifies one or more elements of interest. The textual identification system then causes presentation of the elements of interest in a first portion of a graphical user interface and the textual identifications for the set of textual data in a second portion of the graphical user interface.

Analysis of large bodies of textual data

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Jersey City, NJ, United States of America

Paul Gribelyuk