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BentoML Documenation

BentoML makes it easy to serve and deploy machine learning models in the cloud.

It is an open source framework for machine learning teams to build cloud-native prediction API services that are ready for production. BentoML supports most popular ML training frameworks and common deployment platforms including major cloud providers and docker/kubernetes.

👉 Join BentoML Slack community to hear about the latest development updates.

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• Getting Started
  • Installing BentoML
  • Hello World
  • Model Serving with BentoML
  • Containerize REST API server with Docker
  • Deploy REST API server to the cloud
• Main Concepts
  • Introducing BentoService
  • Packaging Model Artifacts
  • BentoService Handlers
  • Loading SavedBundle
  • Model Management
  • Deploying BentoService
• Advanced Guides
  • Monitoring with Prometheus
  • Request Logging
  • Adaptive Micro Batching
  • Customizing API Server Docker Container
  • Serving Multiple Models
  • Adding Custom Model Artifact
  • Customizing BentoHandler
• Deployment Management
  • Deploying to AWS Lambda
  • Deploying to AWS SageMaker
  • Deploying to Clipper Cluster
  • Deploying to Kubernetes Cluster
  • Deploying to AWS ECS(Elastic Container Service)
  • Deploying to Google Cloud Run
  • Deploying to Azure Container Instance
• API Reference
  • BentoService API
  • Model Artifacts
  • API Handlers
• CLI Reference
  • bentoml