Operationalize generative AI to improve efficiency at your agency
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Agencies are rapidly investing in AI and machine learning (ML) to enhance mission readiness and operational efficiency. However, fragmented data ecosystems, governance complexities, and uncertainty on how to move from pilot to production can present challenges to scaling AI effectively.
This session showcases how a unified MLOps platform and strategy can provide a scalable and secure foundation for operationalizing AI.
Key Takeaways Include:
- Mission-Driven AI Scalability – Learn how to seamlessly integrate AI across mission-critical operations, balancing speed, security, and accuracy.
- Flexible & Secure AI Infrastructure – Explore scalable MLOps solutions that eliminate inefficiencies caused by siloed systems and legacy infrastructure.
- Dynamic Model Adaptation – Leverage the ability to switch between multiple LLMs and vector databases to optimize performance for different mission scenarios.
- Trusted AI for Decision Superiority – Ensure AI models deliver precise, explainable, and actionable insights for both mission and operational use cases.
Featured Speakers
Thad Goodwyn
Director of Gen AI
Machine learning principal investigator and sector chief software architect specializing in complex algorithm implementation--specifically in the machine learning arena. Has applied statistical and machine learning background to a wide range of problems, including text analysis, image processing and advanced discrimination algorithms.
Jarrod Vawdrey
Field chief data scientist
Jarrod Vawdrey is a fixture in the data science community, and is Advisor and former CEO of A42 Labs, a leading provider of AI and ML software and services for building, deploying and managing business critical data workloads at scale. Jarrod holds multiple patents in the field of AI and data science.
Andrea Lowe
Sales Engineer
Dr. Andrea Lowe is a data scientist, engineering leader, and research scientist who leads customer success for government agencies, at Domino. She has led project collaborations with both industry and government, and her recent projects involved statistical analysis of large data sets to evaluate the effectiveness of a rodent-size transcranial magnetic stimulator.