compose VS snorkel

Compare compose vs snorkel and see what are their differences.

compose

A machine learning tool for automated prediction engineering. It allows you to easily structure prediction problems and generate labels for supervised learning. (by alteryx)
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compose snorkel
1 6
510 5,972
0.0% 0.3%
0.0 5.2
about 1 year ago about 2 months ago
Python Python
BSD 3-clause "New" or "Revised" License 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.

compose

Posts with mentions or reviews of compose. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-31.

snorkel

Posts with mentions or reviews of snorkel. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-03.

What are some alternatives?

When comparing compose and snorkel you can also consider the following projects:

trainset - A lightweight web application for brushing labels onto time series data; useful for building training sets.

argilla - Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets

snorkel - A system for quickly generating training data with weak supervision [Moved to: https://github.com/snorkel-team/snorkel]

cleanlab - Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

hover - :speedboat: Label data at scale. Fun and precision included.

dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.

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