A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
-
Updated
Apr 15, 2023
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Unified Model Serving Framework
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
Frouros is an open source Python library for drift detection in machine learning systems.
Chassis turns machine learning models into portable container images that can run just about anywhere.
A modern, enterprise-ready business intelligence web application
Machine Learning Engineering for Production (MLOps) Coursera Specialization
Python library for Modzy Machine Learning Operations (MLOps) Platform
The official JavaScript SDK for the Modzy Machine Learning Operations (MLOps) Platform.
Sample notebooks that use the Openlayer Python API
A curated list of resources to deep dive into the intersection of applied machine learning and threat detection.
The official Java library for the Modzy Machine Learning Operations (MLOps) Platform
Curated set of MLOps tools to work with the Neu.ro MLOps platform
The Golang library for Modzy Machine Learning Operations (MLOps) Platform
This project contains the production-ready Machine Learning solution for detecting and classifying Covid-19, Viral disease, and No disease in posteroanterior and anteroposterior views of chest x-ray
A framework for conducting MLOps.
The project comprises a real-time tweets data pipeline, a sentimental analysis of the tweets module, and a Slack bot to post the tweets' sentiments. The project uses SentimentIntensityAnalyzer from the VaderSentiment library. The analyzer gives positive, negative, and compound scores for small texts (such as tweets in this case). The real-time d…
A curated list of awesome open source tools and commercial products that will help you train, deploy, monitor, version, scale, and secure your production machine learning on kubernetes
In this tutorial we'll bring the TensorFlow 2 Quickstart to Valohai, taking advantage of Valohai versioned experiments, data inputs, outputs and exporting metadata to easily track & compare your models.
Add a description, image, and links to the machine-learning-operations topic page so that developers can more easily learn about it.
To associate your repository with the machine-learning-operations topic, visit your repo's landing page and select "manage topics."