serving

Here are 84 public repositories matching this topic...

ray-project / ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.

python java data-science machine-learning reinforcement-learning deep-learning deployment tensorflow optimization parallel pytorch distributed model-selection hyperparameter-optimization ray automl hyperparameter-search serving rllib

Updated Oct 21, 2022
Python

tensorflow / serving

Star

A flexible, high-performance serving system for machine learning models

python machine-learning deep-neural-networks deep-learning neural-network cpp tensorflow ml serving

Updated Oct 21, 2022
C++

vespa-engine / vespa

Star

The open big data serving engine. https://vespa.ai

java search-engine machine-learning big-data ai server cpp tensorflow vespa serving serving-recommendation vector-search

Updated Oct 21, 2022
Java

ahkarami / Deep-Learning-in-Production

Star

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

Updated Oct 14, 2022

SeldonIO / seldon-core

Star

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

kubernetes machine-learning deployment serving aiops production-machine-learning mlops machine-learning-operations

Updated Oct 21, 2022
HTML

pytorch / serve

Star

Serve, optimize and scale PyTorch models in production

docker kubernetes machine-learning cpu deep-learning metrics gpu optimization pytorch serving mlops

Updated Oct 21, 2022
Java

tobegit3hub / tensorflow_template_application

Star

TensorFlow template application for deep learning

machine-learning csv deep-learning tensorflow inference cnn lstm tensorboard mlp libsvm tfrecords wide-and-deep serving

Updated Oct 4, 2022
Python

Delta-ML / delta

Star

DELTA is a deep learning based natural language and speech processing platform.

Updated Aug 10, 2022
Python

tobegit3hub / simple_tensorflow_serving

Star

Generic and easy-to-use serving service for machine learning models

http client machine-learning deep-learning tensorflow tensorflow-models serving savedmodel

Updated Jan 3, 2021
JavaScript

PaddlePaddle / Serving

Star

A flexible, high-performance carrier for machine learning models（『飞桨』服务化部署框架）

python docker deep-learning pipeline gpu prediction micro-service rpc-service dag paddle microservice-toolkit predictor serving online-service paddle-serving

Updated Oct 12, 2022
C++

meta-soul / MetaSpore

Star

A unified end-to-end machine intelligence platform

training ai machinelearning deeplearning abtesting serving

Updated Oct 21, 2022
Python

openvinotoolkit / model_server

Star

A scalable inference server for models optimized with OpenVINO™

kubernetes machine-learning cloud ai deep-learning inference edge dag model-serving serving openvino

Updated Oct 21, 2022
C++

bodywork-ml / bodywork-core

Star

ML pipeline orchestration and model deployments on Kubernetes, made really easy.

python kubernetes devops data-science machine-learning framework pipeline continuous-deployment orchestration batch cicd serving mlops

Updated Jul 5, 2022
Python

underneathall / pinferencia

Star

Python + Inference - Model Deployment library in Python. Simplest model inference server ever.

Updated Aug 26, 2022
Python

Hydrospheredata / hydro-serving

Star

MLOps Platform

machine-learning spark serverless tensorflow scikit-learn models realtime pipelines scoring serving

Updated Jan 24, 2022
Mustache

zzsza / Boostcamp-AI-Tech-Product-Serving

Star

[Machine Learning Engineer Basic Guide] 부스트캠프 AI Tech - Product Serving 자료

serving mlops

Updated Jul 12, 2022
Jupyter Notebook

outcaste-io / outserv

Star

Blockchain Search with GraphQL APIs

search graphql search-engine system decentralized web3 serving

Updated Oct 7, 2022
Go

cap-ntu / ML-Model-CI

Star

MLModelCI is a complete MLOps platform for managing, converting, profiling, and deploying MLaaS (Machine Learning-as-a-Service), bridging the gap between current ML training and serving systems.