Machine Learning and Deep Learning for Mission Applications

Design and deployment of machine learning and deep learning pipelines that transform raw data into actionable predictions — from data engineering through model training, validation, and deployment in secure environments.

PRACTICE

Applied AI & Data Analytics

CLIENT PROFILE

DoD or IC organization developing predictive models and classification systems for mission-critical applications such as threat detection, predictive maintenance, or pattern recognition

Challenge

Data scientists spending more time on data wrangling than model development
No standardized ML pipeline for reproducible training, testing, and deployment
Models developed in research environments that cannot be operationalized in production

Approach

Build automated data pipelines for ingestion, cleansing, and feature engineering
Establish ML platform with experiment tracking, model registry, and version control
Implement MLOps practices for model validation, deployment, and monitoring in production
Design model governance framework including bias testing, explainability, and performance monitoring

Typical Outcomes

Reduced time from concept to deployed model through standardized ML pipelines
Reproducible experiments with tracked parameters, data versions, and results
Production-grade model serving with monitoring and automated retraining triggers

Procurement Paths

NASA SEWP V for GPU compute and ML infrastructure
GSA MAS for data science and ML engineering services
CIO-CS (NITAAC) for enterprise analytics platforms

Partner Technology Examples

NVIDIA
AMD
AWS GovCloud
Microsoft Azure

Odin's Edge — For When the Cloud Isn't an Option

Norseman's Odin's Edge is a fully autonomous edge AI platform delivering AI inferencing, ruggedized storage, private 5G, containerized desktops, and ServiceNow ITSM in a single field-deployable unit — engineered for DDIL (Disconnected, Degraded, Intermittent, and Limited bandwidth) environments.

Explore Odin's Edge →

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