zoom_in Click to enlarge

BentoML

▲ ▼

language

description BentoML Overview

BentoML is a framework for packaging and deploying machine learning models as scalable APIs. It simplifies the process of creating production-ready endpoints, enabling fast and reliable model serving. BentoML's containerization capabilities ensure portability and reproducibility. Its focus on performance and scalability makes it suitable for high-traffic applications.

help BentoML FAQ

How does a Python model become an API in BentoML?

A developer defines a BentoML Service and exposes Python functions as API endpoints. The service can then be run locally, packaged and deployed behind an HTTP interface.

Does BentoML require models from one specific framework?

No. BentoML can serve custom Python inference code and models from ecosystems such as PyTorch, TensorFlow, Hugging Face and MLflow.

What is the difference between BentoML and BentoCloud?

BentoML is the open-source Python serving framework distributed through PyPI. BentoCloud is BentoML's managed inference and compute-orchestration platform for deploying and scaling those services.

Can BentoML package a service into a Docker container?

Yes. A Bento bundles the service code, model references and dependencies into a reproducible deployable artifact that can be containerized for production.

swap_horiz

Looking for BentoML alternatives? Compare top competitors ranked & scored

arrow_forward