Popular GitHub Repositories for Tensorflow Serving

Adlik: Toolkit for Accelerating Deep Learning Inference

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.

ClearML - Model-Serving Orchestration and Repository Solution

Kafka Streams + Java + gRPC + TensorFlow Serving => Stream Processing combined with RPC / Request-Response

export bert model for serving

YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving

A tutorial of building tensorflow serving service from scratch

Implement some hair segmentation network and a color similarity calculating method.

A curated list of awesome inference deployment framework of artificial intelligence (AI) models. OpenVINO, TensorRT, MediaPipe, TensorFlow Lite, TensorFlow Serving, ONNX Runtime, LibTorch, NCNN, TNN, MNN, TVM, MACE, Paddle Lite, MegEngine Lite, OpenPPL, Bolt, ExecuTorch.

Use BERT to train a classification model and deploy the model by tensorflow serving

Serving TensorFlow models with TensorFlow Serving :orange_book:

bert sentiment analysis tensorflow serving with RESTful API

Tools and libraries for using Tensorflow (and Tensorflow Serving) in go

JuPyter Notebooks and Python Package for Deep Learning Model Exploration, Translation and Deployment

Simple examples of serving HuggingFace models with TensorFlow Serving

Tensorflow Serving with Docker / Docker Compose

Easily Deploy your Tensorflow models to Heroku with just the click of a button!

This project is an amalgamation of research (federated training and comparison with normal training), development (data preprocessing, model training etc.) and deployment (model serving). It creates a pipeline that trains models using federated learning and deploys them using tensorflow serving and docker

Built an AI on the edge platform for the developer to deploy a full-fledged TensorFlow based AI application where data is gathered from the edge devices as per developer’s requirement. The platform was built using micro-service based architecture with features like auto-scaling, scheduling, centralized logging, monitoring, notification, high-availability and fault tolerance.

This repository exemplifies a robust ML workflow, leveraging MLflow for experiment tracking, Docker for containerization, TensorFlow Serving for model deployment, and GitHub Actions for CI/CD. It embodies a comprehensive system designed to predict diabetes progression using advanced machine learning paradigms.