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mkurman/zorai/bentoml

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PublishedJuly 29, 2026 at 12:07 PM

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version: "1.0.0" 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

bash

uv pip install bentoml

Service Definition

python

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

bash

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

References

All versions