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BentoML: The Unified Framework for Production AI/ML Serving
BentoML is an open-source framework for building, shipping, and scaling production AI applications. Easily serve ML models as APIs, create job queues, build LLM apps, and orchestrate multi-model inference pipelines.
Python
Added on 2025年7月4日7,848
Stars850
ForksPython
LanguageProject Introduction
Summary
BentoML is a unified framework for putting your machine learning models into production. It handles packaging your models, code, and dependencies into 'Bentos', which can then be served as real-time APIs or offline jobs, and deployed to various environments.
Problem Solved
Deploying machine learning models to production is complex, involving dependency management, API creation, scaling, monitoring, and orchestration. BentoML provides a streamlined, framework-agnostic workflow to address these challenges.
Core Features
Model Packaging
Package ML models and their dependencies into production-ready formats called 'Bentos'.
API Serving
Quickly generate high-performance REST APIs for your trained models with minimal code.
Job Queues
Run models for batch processing or asynchronous tasks using built-in job queue capabilities.
Multi-Model Pipelines
Orchestrate complex inference graphs and multi-step prediction workflows.
LLM Serving
Built-in support and optimizations for serving Large Language Models.
Flexible Deployment
Deploy Bentos easily to various platforms, from local Docker to Kubernetes and cloud services.
Tech Stack
Python
Docker
Kubernetes
ASGI/WSGI
Containerization
Use Cases
BentoML can be used in various scenarios for deploying machine learning models and AI applications.
Real-time Model Serving
Details
Deploy a trained image classification model as a high-throughput API endpoint for real-time inference from a web or mobile application.
User Value
Provides low-latency predictions and scales automatically with demand.
Batch Processing & Offline Inference
Details
Process large datasets using a model for tasks like fraud detection or image analysis in a batch mode, triggered periodically or by events.
User Value
Efficiently processes large volumes of data without needing a continuous, active endpoint.
Building Multi-Model Inference Pipelines
Details
Build and deploy complex applications that involve multiple AI models running sequentially or in parallel, such as a document processing pipeline involving OCR, text classification, and entity extraction.
User Value
Simplifies the orchestration and deployment of complex AI workflows as a single service.
Serving Large Language Models (LLMs)
Details
Quickly package and serve fine-tuned Large Language Models (LLMs) or integrate with major LLM providers, handling specific LLM serving challenges.
User Value
Optimized for LLM serving, reducing complexity and infrastructure requirements.
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