Apply Multi-Node Multi-GPU Computing to HPO and Inference

With ever-increasing amounts of data and limited compute resources, training competitive ML models end-to-end can take hours, days, or even weeks. Parallel computing offers a solution. Join us to learn how to reduce your end-to-end ML pipeline and increase model accuracy by parallelizing and distributing model training, hyperparameter optimization, and inference across GPUs.
Prerequisite(s):

Foundational understanding of a data science workflow on tabular data.
Experience with programming in Python.
Experience with the Pandas and scikit-learn API.