Blog

Decision Optimization with GPU acceleration: In conversation with the NVIDIA cuOpt team

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.

Operationalizing Python decision models: configurable options, simple I/O, custom logging, and more

If you’re building decision models in Python, our Python SDK and decision science platform make the development process faster (and easier) so you can get your model safely into production.

In conversation with the HiGHS project developers

What is HiGHS? How is it used for MIP solving? Who’s using HiGHS? And what’s next for this open source project? We spoke with the creators of the HiGHS project to find out.

Observability & decision science: Monitoring optimization model performance and more

When decision models power real-life operations, any sort of model performance failure is a nightmare. Learn why observability in the operations research space is often a challenge – and how to give your team more visibility into model performance with DecisionOps.

Simulate “what if” questions for decision models with scenario testing and Nextmv

What if order volume increases 4x? What if I changed shift length? What’s the best model formulation? Efficiently play out different scenarios under realistic conditions before committing to a plan using Nextmv’s scenario testing capabilities.

The sushi is ready. How do I deliver it? A look at the behind-the-scenes logistics.

We examine the value of treating decision models as engineered software components and how to approach decision modeling with an adaptable process.