The path to production is a journey that necessitates applying software engineering practices to mathematical modeling. Learn about ways to demystify and streamline that process in this panel discussion.
What does agility look like in the decision science space? Learn about the factors that accelerate decision model development and prototyping, ensure quality, and deliver project value.
Behind every optimization project, there’s a compelling origin story to be told. Learn about tough (and valuable!) lessons in practicing OR from the folks who’ve lived them.
How are open source projects being used today? How should the community think about adoption and participation? Hear from members of the Pyomo and HiGHS teams.
Human review and feedback is part of any good decision workflow. Learn what decision algorithm developers and teams can do to increase trust and build confidence.
How can GPUs improve decision optimization workflows? In what ways will solving optimization problems change? How does this change the way technology leaders think about their AI strategies? We spoke with the NVIDIA cuOpt team to find out.
Advice and perspectives from Ryan O’Neil and Thiago Serra about moving from academic settings and research environments to industry practice.
Explore the applications of mathematical optimization alongside the AI/ML landscape for predictive analytics, LLMs, and beyond.
This interview explores perspectives on the intersection of ML and OR, challenges and opportunities for data scientists in the optimization space, observations in industry, and why now is a great time for practitioners to expand their skillset.
This interview explores perspectives on growth and adoption of optimization across practitioners, challenges and opportunities in the optimization space, thinking about optimization in broader AI strategies, and DecisionOps + Hexaly.
