A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Always know what to expect from your data.

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

Model Serving Made Easy

A curated list of references for MLOps

MLOps examples

MLOps using Azure ML Services and Azure DevOps

Collective Knowledge (CK) is a small Python library that helps to decompose complex research projects into reusable and cross-linked components (wrappers for code, data, models and results), automation actions and portable workflows with unified APIs, CLI, and JSON I/O. You can find more info and real-world use cases at the project website:

An introduction to machine learning model deployment operations (MLOps) using Python, Docker, Kubernetes and Seldon-Core.

Data transformations for the ML era

TRAINS Agent - Auto-Magical DevOps for Deep Learning (Simple AI Cluster Solution)

A Collection of GitHub Actions That Facilitate MLOps

AI開発プラットフォームKAMONOHASHI

Operationalize a video anomaly detection model with Azure ML

CDF SIG MLOps

Docker images for fastai

Template for getting started with automated ML Ops on Azure Machine Learning

😎 A curated list of awesome MLOps tools

Kubeflow Pipeline with Tekton backend. Enables SDK functionalities (Compile, Upload, Launch) and KFP UI with execution and artifact tracking

ML DevOps using GitHub Actions and Azure Machine Learning

Python Machine Learning (ML) project that demonstrates the archetypal ML workflow within a Jupyter notebook, with automated model deployment as a RESTful service on Kubernetes.

Predicts COVID-19 from PA view of X-ray when submitted on the website

ML.NET Workshop to predict car sales prices

CartPole game by Reinforcement Learning, a journey from training to inference

MLOps Case Study

Aim — a super-easy way to record, search and compare AI experiments

A machine learning model operations and management tool for ML.NET

Algorithm engineering is hard enough: don't spend your time with logistics. QA-Board organizes your runs and lets you visualize, compare and share results.

Kubeflow pipelines built on top of Tensorflow TFX library