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80 public repositories
matching this topic...
Unified Model Serving Framework 🍱
Updated
Aug 20, 2022
Python
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Serverless Inferencing on Kubernetes
Updated
Aug 19, 2022
Python
Machine Learning automation and tracking
Updated
Aug 20, 2022
Python
Hopsworks - Data-Intensive AI platform with a Feature Store
Updated
Aug 19, 2022
Java
Production-first ML platform on Kubernetes 🦄️
Updated
Aug 19, 2022
TypeScript
Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
Updated
Aug 18, 2022
Python
FastAPI Skeleton App to serve machine learning models production-ready.
Updated
Jun 22, 2022
Python
Code samples for the Lightbend tutorial on writing microservices with Akka Streams, Kafka Streams, and Kafka
Updated
May 30, 2019
Scala
A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
Updated
Jun 27, 2022
Python
Common library for serving TensorFlow, XGBoost and scikit-learn models in production.
Updated
Aug 15, 2022
Java
BentoML Example Projects 🎨
Updated
Aug 4, 2022
Python
A scalable, high-performance serving system for federated learning models
Updated
Aug 11, 2022
Java
A high-performance serving framework for ML models, offers dynamic batching and multi-stage pipeline to fully exploit your compute machine
Updated
Aug 20, 2022
Python
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
Updated
May 9, 2019
Scala
Serving PyTorch models with TorchServe 🔥
Updated
Feb 28, 2021
Jupyter Notebook
Deploy DL/ ML inference pipelines with minimal extra code.
Updated
Jul 19, 2022
JavaScript
Code and presentation for Strata Model Serving tutorial
Updated
Sep 26, 2019
Scala
🚀 Stream inferences of real-time ML models in production to any data lake
Updated
Jun 10, 2022
Python
A collection of model deployment library and technique.
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