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Trent D Mcdaniel, 53201 Alta Ave, San Antonio, TX 78209

Known as:
Trent David Mcdaniel Trent P Mcdaniel David T Mcdaniel Trent David Mc Daniel Trent Dmcdaniel Trent Mc
Related to:
Patricia Mc Daniel Carla Gianetti 52 Robert J Mccleery 79 Jayne Mccabe 77 Robert J Chadwell 50

Trent Mcdaniel Phones & Addresses

201 Alta Ave, San Antonio, TX 78209   

Alamo Heights, TX   

DPO, AE   

Arlington, VA   

Helotes, TX   

Atlanta, GA   

Tampa, FL   

Brandon, FL   

Valrico, FL   

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Mentions for Trent D Mcdaniel

Trent Mcdaniel resumes & CV records

Resumes

Trent Mcdaniel Photo 27

Co-Founder

Location:
San Antonio, TX
Industry:
Computer Software
Work:
Epiphany Search Apr 1999 - Aug 2004
Solutions Architect Quickpath Apr 1999 - Aug 2004
Co-Founder Epiphany Ssa Global Infor 1999 - 2004
Solution Architect Meta4 1996 - 1998
Developer
Education:
University of South Florida 1988 - 1993
Masters, Master of International Studies, Management
Skills:
Saas, Business Intelligence, Enterprise Software, Cloud Computing, Crm, Soa, Analytics, Sdlc, Agile Methodologies, Business Analysis, Requirements Analysis, Marketing Automation, Professional Services, Enterprise Architecture, Integration, Management, Software Project Management, Ibm Unica Campaign, Go To Market Strategy, Customer Relationship Management, Software As A Service, Software Development Life Cycle, Ibm Unica Interact, Ibm Unica Marketing Operations, Ibm Spss Modeler, Ibm Spss Collaboration and Deployment Services, Ibm Spss Scoring Server
Trent Mcdaniel Photo 28

Trent Mcdaniel

Trent Mcdaniel Photo 29

Trent Mcdaniel

Publications & IP owners

Us Patents

Real-Time Drift Detection In Machine Learning Systems And Applications

US Patent:
2020008, Mar 12, 2020
Filed:
Sep 6, 2019
Appl. No.:
16/563805
Inventors:
- San Antonio TX, US
Trent McDaniel - San Antonio TX, US
International Classification:
G06N 20/00
G06F 17/18
G06F 11/32
Abstract:
The present disclosure is for systems and methods for connecting offline machine learning training systems with online near-real time machine learning scoring systems. It is not trivial to connect an offline training environment with an online scoring environment. For example, offline training environments are usually static and contain large amounts of historical data that is needed for the initial training of models. Once trained, the model algorithms are then migrated into an online scoring environment for transactional or event based scoring. This migration effectively breaks the connection between the data in the offline environment and the model now running in the online environment. When new or shifting data occurs in the online environment, the static model running in the online environment goes unaltered to the changing inputs. The present disclosure solves the issues that are caused by the break in the offline and online environments.

Model Score Recall For Machine Learning Systems And Applications

US Patent:
2019033, Oct 31, 2019
Filed:
Apr 26, 2019
Appl. No.:
16/396605
Inventors:
- San Antonio TX, US
Trent McDaniel - San Antonio TX, US
International Classification:
G06F 16/2457
G06N 20/00
Abstract:
The present disclosure is for a system and a method for processing scoring request in a machine learning system in a computationally efficient and low-cost manner to enable near real-time scoring of incoming scoring requests. Specifically, the present invention is a for a model score recall system and method that enables a system to recall model scores for input rows of cross features values that have already been scored a machine learning model without having to perform a search on very large datasets. As such, the present disclosure provides a system and a method for obtaining scores provided by a machine learning model without having to run the model on each new incoming scoring request, which saves computational resources and saves costs associated with performing a compute transaction.

Possible Relatives

Cynthia D Mcdaniel  David L Mcdaniel  Dawn R Mcdaniel  Dayna A Mcdaniel  Dean Mcdaniel  Deana M Mcdaniel  Deann R Mcdaniel  Trent Johnson  Trent M Jones  Trent E Mcintire 

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