From Commercial to Combat-Ready

This event qualifies for:

1 CPEs
Spectro Cloud, Domino Data Lab and Carahsoft are excited to invite you to join us subsequent to Sea-Air-Space 2026 to explore how your team can innovate, adapt, and collaborate in service of your mission goals by utilizing emerging technologies. 
 
Defense organizations are rapidly adopting AI to support mission-critical decisions across unmanned systems, ISR, autonomy, and logistics. Yet many AI initiatives stall after models are developed. The real challenge isn’t building AI - it’s deploying, governing, and sustaining it across cloud, data centers, classified networks, and tactical edge environments where connectivity may be degraded or unavailable.

In this session, we’ll explore how Spectro Cloud and Domino Data Lab together provide a mission-ready AI backbone and architecture that turns AI projects into operational capabilities.

Attendees will learn how to:

  • Apply proven successes from our work supporting the US Navy's unmanned underwater vehicles (UUVs) and unmanned aerial systems (UAS) programs
  • Accelerate AI fielding from development to operational deployment, in days
  • Operate AI reliably across cloud, data center, classified, and edge environments
  • Maintain governance and compliance while scaling AI across missions
  • Safely update models and platforms without disrupting operations
The result: AI that moves from prototype to operational capability - securely, reliably, and wherever the mission requires.
 
Register today!

Speaker Details

Mark Shayda

Senior Systems Engineer
Spectro Cloud

Nick Jablonski

Field CTO
Domino Data Lab

Event Topic

Artificial Intelligence, Data Center / Infrastructure, Technology

Relevant Audiences

All Military, All State and Local Government, All Federal Government
From Commercial to Combat-Ready
Event Type
Virtual / Online
Event Subtype
Webinar / Webcast
When
Tue, Apr 28, 2026 | 1:00 pm - 2:00 pm ET
Registration Cost
Complimentary
Website
Click here to view event website
Sponsors
Spectro Cloud