MLOps

Description

“ML Operations” is the combination of “DevOps” and Machine Learning. Tools in the MLOps space provide the infrastructure to put machine learning models in production. These tools may include training and testing of models, a generic pipeline/workflow mechanism, model registries, scalable model serving via HTTP, and metrics capture.

Projects

4

Lines Committed vs. Age Chart (click to view)

Lines Committed vs. Age Chart (click to view)

Projects

Project

Size Score

Trend Score

Byline

Kubeflow

7.75

3.5

The Machine Learning Toolkit for Kubernetes

Metaflow

3.75

9.0

Build and manage real-life data science projects with ease

MLflow

9.25

8.75

An open source platform for the machine learning lifecycle

Polyaxon

8.0

4.0

Machine Learning Platform for Kubernetes (MLOps tools for experimentation and automation)