Easy AI Computational Benchmarking Across Multiple Cloud Resources
Determining the most efficient cloud hardware for training, evaluating, or deploying a deep learning model can be time-consuming, and if the model runs on poorly chosen resources, the cost can be high. Historically, benchmarking AI model computational performance required sophisticated infrastructure or expensive SAAS products, which are often out of reach for teams without dedicated DevOps expertise or deep pockets.
