MACHINE LEARNING OPERATIONS (MLOPS) ARCHITECTURE CONSIDERATIONS FOR DEEP LEARNING WITH A PASSIVE ACOUSTIC VECTOR SENSOR
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Authors
Villemez, Nicholas R.
Subjects
acoustic
deep learning
automated identification system
AIS
classification
live inference
model deployment
machine learning architecture
machine learning operations
production
machine learning
artificial intelligence
Bayesian
neural network
deep learning
automated identification system
AIS
classification
live inference
model deployment
machine learning architecture
machine learning operations
production
machine learning
artificial intelligence
Bayesian
neural network
Advisors
Leary, Paul
Orescanin, Marko
Date of Issue
2021-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
As machine learning augmented decision-making becomes more prevalent, defense applications for these techniques are needed to prevent being outpaced by peer adversaries. One area that has significant potential is deep learning applications to classify passive sonar acoustic signatures, which would accelerate tactical, operational, and strategic decision-making processes in one of the most contested and difficult warfare domains. Convolutional Neural Networks have achieved some of the greatest success in accomplishing this task; however, a full production pipeline to continually train, deploy, and evaluate acoustic deep learning models throughout their lifecycle in a realistic architecture is a barrier to further and more rapid success in this field of research. Two main contributions of this thesis are a proposed production architecture for model lifecycle management using Machine Learning Operations (MLOps) and evaluation of the same on live passive sonar stream. Using the proposed production architecture, this work evaluates model performance differences in a production setting and explores methods to improve model performance in production. Through documenting considerations for creating a platform and architecture to continuously train, deploy, and evaluate various deep learning acoustic classification models, this study aims to create a framework and recommendations to accelerate progress in acoustic deep learning classification research.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
Identifiers
NPS Report Number
Sponsors
Los Alamos National Lab
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.