We will highlight the power of hybrid probabilistic deep learning by discussing how this approach is used for building system-of-system models for large-scale systems such as refineries, power generation systems, and gas compression systems. We'll cover how GPUs accelerate all three applications, with a focus on a time series prediction model for predicting overall production in a large oil field with multiple changing parameters.
This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.