bentoml

name	bentoml
description	BentoML — model serving and deployment. Build prediction services from any ML framework with OpenAPI/Swagger. Containerize, deploy to Kubernetes, AWS, GCP, Azure. Adaptive batching and GPU support.
tags	["bentoml","model-serving","deployment","mlops","api","kubernetes","zorai"]

Overview

BentoML packages ML models with service definitions, dependencies, environment config, and deployment targets into a portable "Bento." Deploy to Kubernetes (Kserve, Seldon), AWS SageMaker, GCP Vertex AI, or as a standalone Docker container.

Installation

uv pip install bentoml

Service Definition

import bentoml
from bentoml.io import JSON
import numpy as np

iris_clf = bentoml.sklearn.get("iris_model:latest")

@bentoml.service
class IrisClassifier:
    def __init__(self):
        self.model = iris_clf.to_runner()
        self.model.init_local()

    @bentoml.api(input=JSON(), output=JSON())
    def classify(self, input_data):
        result = self.model.run(np.array([input_data["features"]]))
        return {"class": int(result[0]), "probabilities": result[1].tolist()}

Build & Deploy

bentoml build      # creates a Bento
bentoml containerize iris_classifier:latest  # Docker image
docker run -p 3000:3000 iris_classifier:latest

References

• BentoML docs
• BentoML GitHub