Paddle Serving helps deep learning developers deploy an online inference service without much effort. **The goal of this project**: once you have trained a deep neural nets with Paddle, you already have a model inference service.
Paddle Serving helps deep learning developers deploy an online inference service without much effort. **The goal of this project**: once you have trained a deep neural nets with [Paddle](https://github.com/PaddlePaddle/Paddle), you already have a model inference service.
## Key Features
## Key Features
- Integrate with Paddle training pipeline seemlessly, most paddle models can be deployed **with one line command**.
- Integrate with Paddle training pipeline seemlessly, most paddle models can be deployed **with one line command**.
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-**Distributed Key-Value indexing** supported that is especially useful for large scale sparse features as model inputs.
-**Distributed Key-Value indexing** supported that is especially useful for large scale sparse features as model inputs.
-**Highly concurrent and efficient communication** between clients and servers.
-**Highly concurrent and efficient communication** between clients and servers.
-**Multiple programming languages** supported on client side, such as Golang, C++ and python
-**Multiple programming languages** supported on client side, such as Golang, C++ and python
-**Extensible framework design** that can support model serving beyond Paddle.
