EthicML VS verifyml

Compare EthicML vs verifyml and see what are their differences.

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EthicML verifyml
1 1
24 22
- -
9.3 0.0
4 days ago about 2 years ago
Python Python
GNU General Public License v3.0 only 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.

EthicML

Posts with mentions or reviews of EthicML. We have used some of these posts to build our list of alternatives and similar projects.

verifyml

Posts with mentions or reviews of verifyml. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-10.
  • Building a Responsible AI Solution - Principles into Practice
    6 projects | dev.to | 10 Jan 2022
    Interested readers should check out the VerifyML website, docs or Github code. Feel free to create a Github issue and drop any suggestions or feedback over there! VerifyML is proudly open-sourced and created by a small tech startup. I would like to think that more companies are going to see responsible AI as a comparative advantage or requirement and having an ecosystem of solutions that are not controlled by the interests of large tech companies would be key in driving the sector forward. I look forward to improving the user experience and integration with more machine learning tools over the next year, as well as sharing more thoughts in the space.

What are some alternatives?

When comparing EthicML and verifyml you can also consider the following projects:

responsible-ai-toolbox - Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

fairlearn - A Python package to assess and improve fairness of machine learning models.

DALEX - moDel Agnostic Language for Exploration and eXplanation

AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]

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

pygod - A Python Library for Graph Outlier Detection (Anomaly Detection)

Jenkins - Jenkins automation server

model-card-toolkit - A toolkit that streamlines and automates the generation of model cards