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ML Ops Platform

Modernizing Mission AI with NextGen’s StratusML

Delivering compliant, end-to-end model development, deployment, and monitoring across high-stakes mission environments.

A diverse team of U.S. Air Force personnel and civilian researchers collaborates in a weather innovation lab
A software engineer monitors machine learning model deployment from a multi-screen workstation in a U.S. Air Force operations center
ML OPS_Map

Project Overview

NextGen Federal’s ML Ops Platform was built to address a critical challenge across federal operations: the need to reliably build, deploy, and manage machine learning models in environments demanding strict security, operational consistency, and continuous validation.


The platform centralizes the entire ML lifecycle — development, testing, deployment, execution, evaluation, and monitoring — within a secure, governed framework designed for mission conditions. This includes capabilities highlighted in prior briefing materials, such as the ability to:

  • Manage: datasets, parameters, and models
  • Execute: training, prediction, and evaluation
  • Monitor: pipeline processes
  • Evaluate: models based on mission-relevant metrics

By delivering a standardized AI infrastructure, the ML Ops Platform enables agencies to move from prototype to mission-approved systems with speed, confidence, and full traceability.

Customer Need

A modern web portal for Air Force Weather (AFW) product dissemination, including tools to distribute, evaluate, and manage weather-focused ML outputs at enterprise scale.

Method & Strategy

NextGen Federal approached the ML Ops Platform with a strategy anchored in operational rigor, automation, and mission alignment.

1. Centralized Model Development & Experimentation

A unified environment allows data scientists and mission engineers to collaboratively:

  • Track experiments
  • Version code, models, datasets, and configurations
  • Maintain full lineage and reproducibility

This eliminates fragmented workflows and provides a single source of truth.

2. Automated Pipeline for Testing & Validation

To ensure models meet federal mission requirements, the platform includes:

  • Automated accuracy and robustness tests
  • Security and policy guardrail checks
  • Performance baselines and drift monitoring
  • Required approval workflows

This ensures that every deployed model is verified, documented, and compliant.

3. Controlled Deployment to Mission Environments

Flexible deployment pathways support:

  • Real-time inference engines
  • Batch analytics workflow
  • Edge and disconnected operations
  • On-prem or hybrid cloud mission systems

Deployments include role-based access, model version control, and rollback capabilities.

4. Continuous Operational Monitoring

A persistent monitoring layer evaluates:

  • Model accuracy and stability
  • Data drift and anomalies
  • Latency and performance metrics
  • Unexpected behaviors requiring retraining or rollback

Teams receive alerts and insights to maintain operational effectiveness.

U.S. Air Force weather intelligence dashboard with global weather data, predictive models, and operational monitoring tools.

Main Advantages

Mission-Ready Reliability

Every model is tested, validated, monitored, and tracked, ensuring predictable performance even when mission conditions shift.

Full Security & Compliance Alignment
Supports:

  • FedRAMP-aligned practices
  • Controlled data boundaries and full audit logs

Scalable AI Infrastructure

Deploy and maintain dozens—or hundreds—of models across:

  • Distributed compute clusters
  • Edge devices
  • Hybrid cloud environments

The platform scales alongside evolving mission needs.

Faster AI Delivery Cycles
Automated testing, consistent approval workflows, and standardized pipelines shorten deployment cycles from months to days.

Operational Transparency
Mission teams gain visibility into:

  • How and why models behave as they do
  • Model update frequency
  • What version is deployed and where
  • Performance changes over time

This ensures traceable, explainable AI for federal operations.

Additional Benefits

  • Enables rapid model prototyping
  • Empowers testing and evaluation without requiring ML expertise
  • Supports team and enterprise-wide collaboration
  • Allows sharing data and models in a centralized catalog
  • Accredited for IL5 environments
NextGen Pipeline Workflow

“[NextGen] Thank you for the team you’ve assembled for us. I’ve been incredibly impressed with their ability to produce results and to fill in the gaps when we inevitably overlook some requirements.”

Conclusion

The NextGen Federal ML Ops Platform transforms how agencies build, deploy, and sustain machine-learning capabilities. By unifying development workflows, enforcing rigorous validation, securing deployment pipelines, and monitoring real-time performance, the platform delivers trusted, scalable, mission-aligned AI to federal operations.


Federal teams can now deploy models confidently, respond quickly to changing mission needs, and maintain full oversight across the model lifecycle—ensuring AI performs as reliably as the missions it supports.

Make a Capability Request

  • Innovation

    Secure AI platform driving next-gen mission innovation

  • Technology

    Advanced systems enabling cutting-edge digital capabilities

  • Intelligence

    Real-time insights delivering actionable situational awareness

  • Scalability

    Flexible architecture expanding capacity for growing demands

Let’s Move Forward Together!

Have questions or need support? Our team is here to help you find the right solutions and guide you every step of the way.

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