bentoml
SkillCloud & infraBentoML — 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the bentoml skill
What this skill tells your AI
The instructions your AI receives, as published by mkurman/zorai in skills/scientific-skills/bentoml/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 324
- Forks
- 26
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
bentoml- Source
- github.com/mkurman/zorai