Serverless functions are often used to preprocess data, trigger model training, or handle inference requests. These tasks occur sporadically or in bursts, making them ideal for on-demand execution. Instead of keeping GPU servers running continuously, serverless triggers compute only when needed. This dramatically reduces costs in machine learning workflows. AI pipelines become more modular, scalable, and event-driven as a result.
Serverless lowers the cost barrier for experimenting with AI. Teams can run models without massive infrastructure investment.
This flexibility accelerates innovation in AI, enabling faster experimentation and deployment cycles.
Serverless functions often trigger AI model training and inference workflows.
[NVIDIA Developer Blog, developer.nvidia.com]