polyaxon VS flyte

Compare polyaxon vs flyte and see what are their differences.

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polyaxon flyte
9 31
3,479 4,761
0.7% 4.0%
8.7 9.8
6 days ago 1 day ago
Python Go
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

polyaxon

Posts with mentions or reviews of polyaxon. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-25.

flyte

Posts with mentions or reviews of flyte. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-15.

What are some alternatives?

When comparing polyaxon and flyte you can also consider the following projects:

MLflow - Open source platform for the machine learning lifecycle

metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!

kubeflow - Machine Learning Toolkit for Kubernetes

argo - Workflow Engine for Kubernetes

dvc - πŸ¦‰ ML Experiments and Data Management with Git

temporal - Temporal service

mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]

neptune-client - πŸ“˜ The MLOps stack component for experiment tracking

Celery-Kubernetes-Operator - An operator to manage celery clusters on Kubernetes (Work in Progress)

onepanel - The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.

Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.