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This makes it super easy for your DevOps team to incorporate your modelsīentoML's model management component is called Yatai, it means food cart in Japanese,Īnd you can think of it as where you'd store your bentos 🍱. Prometheus metrics, health check endpoint, prediction logging, and tracing support The model inference API, the containerized BentoML model server also comes with The container image produced will have all the required dependencies installed. Just one command: bentoml containerize MyPredictionService:latest -t my_prediction_service:v3ĭocker run -p 5000:5000 my_prediction_service:v3 -workers 2 save () # saves to $HOME/bentoml/repository/MyPredictionService//Ī docker container image that's ready for production deployment can be created now with pack ( 'my_model', my_sklearn_model ) svc. The entire prediction service at the end, which creates a BentoML bundle: from my_prediction_service import MyPredictionService svc = MyPredictionService () svc. Prediction service class, pack it with your trained model, and call save to persist This can be easily plugged into your model training process: import your bentoml predict ( model_input_df ) return list ( predictions ) A batch API is expect to receive a list of inference input and should returns a list of prediction results. DataFrame ): """ An inference API named `predict` that takes tabular data in pandas.DataFrame format as input, and returns Json Serializable value as output. Prediction service look like in BentoML: import pandas as pd from bentoml import env, artifacts, api, BentoService from bentoml.adapters import DataframeInput, JsonOutput from import SklearnModelArtifact # BentoML packages local python modules automatically for deployment from my_ml_utils import my_encoder ( infer_pip_packages = True ) () class MyPredictionService ( BentoService ): """ A simple prediction service exposing a Scikit-learn model """ ( input = DataframeInput (), output = JsonOutput (), batch = True ) def predict ( self, df : pd.

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Which includes the trained ML model itself, plus its pre-processing, post-processingĬode, input/output specifications and dependencies. To understand which deployment option is best suited for your use case.īentoML provides APIs for defining a prediction service, a servable model so to speak,

Integration with training and experimentation management products including MLFlow, Kubeflow (roadmap)īe sure to check out deployment overview doc.

Advanced model deployment for Kubernetes ecosystem (roadmap).

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Automated offline batch inference job with Dask (roadmap).

Utilities that simplify CI/CD pipelines for ML.

Distributed batch or streaming serving with Apache Spark.

One-click deployment to cloud platforms including AWS EC2, AWS Lambda, AWS SageMaker, and Azure Functions.

Launch offline batch inference job from CLI or Python.

Central repository for managing all your team's prediction services via.

Standardize model serving and deployment workflow for teams:

Health check endpoint and Prometheus /metrics endpoint for monitoring.

Prediction logging and feedback logging endpoint.

Automatically generate REST API spec in Swagger/OpenAPI format.

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Serve any Python code along with trained models.Serve multiple endpoints in one model server.Discover and package all dependencies automatically, including PyPI, conda packages and local python modules.Adaptive micro-batching for optimal online serving performance.Containerized model server for production deployment with Docker, Kubernetes, OpenShift, AWS ECS, Azure, GCP GKE, etc.Support multiple ML frameworks including PyTorch, TensorFlow, Scikit-Learn, XGBoost, and many more.Quickstart Guide, try it out on Google Colab.Serving workloads can integrate with cloud infrastructures.

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Standard interface for describing a prediction service, BentoMLĪbstracts away how to run model inference efficiently and how model

Web dashboards and APIs for model registry and deployment managementīentoML bridges the gap between Data Science and DevOps.

High-Performance online API serving and offline batch serving.

Cloud native deployment with Docker, Kubernetes, AWS, Azure and.

Supports multiple ML frameworks, including Tensorflow, PyTorch, Keras, XGBoost.

BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.