Machine Learning and Deep Learning for Mission Applications

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

Compare vehicles and ordering steps on How to buy.

Partner Technology Examples

• NVIDIA
• AMD
• AWS GovCloud
• Microsoft Azure