model-serving

The Unified Model Serving Framework 🍱

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

Machine Learning automation and tracking

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

FastAPI Skeleton App to serve machine learning models production-ready.

Code samples for the Lightbend tutorial on writing microservices with Akka Streams, Kafka Streams, and Kafka

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

Common library for serving TensorFlow, XGBoost and scikit-learn models in production.

BentoML Sample Projects Gallery 🎨

A scalable, high-performance serving system for federated learning models

flink-jpmml is a fresh-made library for dynamic real time machine learning predictions built on top of PMML standard models and Apache Flink streaming engine

Serving PyTorch models with TorchServe 🔥

Code and presentation for Strata Model Serving tutorial

Deploy DL/ ML inference pipelines with minimal extra code.

A collection of model deployment library and technique.

An umbrella project for multiple implementations of model serving

fastText model serving service

Serving TensorFlow models with TensorFlow Serving 📙

ClearML - Model-Serving Orchestration and Repository Solution

Kubeflow example of machine learning/model serving

A hands-on case study for demonstrating the stages involved in a machine learning project, from EDA to production.

A lightweight machine learning framework for Kubernetes

MXNet Model Serving

💧 Visual Data Preparation (VDP) is an open-source tool to seamlessly integrate Vision AI with the modern data stack

mlserve turns your python models into RESTful API, serves web page with form generated to match your input data.

Titus 2 : Portable Format for Analytics (PFA) implementation for Python 3.4+