integrations-core VS neptune-contrib

Compare integrations-core vs neptune-contrib and see what are their differences.

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integrations-core neptune-contrib
1 -
866 27
1.3% -
10.0 0.0
4 days ago over 1 year ago
Python Python
BSD 3-clause "New" or "Revised" License MIT License
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.

integrations-core

Posts with mentions or reviews of integrations-core. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-28.
  • Opinions on Different Error Monitoring Systems
    3 projects | /r/devops | 28 May 2022
    Good, tell them to fix their fucking agent to stop spamming my logs with their python backtrace. I mean, seriously, who logs a backtrace for a simple connection refused when it's going to poll that endpoint every minute or whatever?

neptune-contrib

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

We haven't tracked posts mentioning neptune-contrib yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing integrations-core and neptune-contrib you can also consider the following projects:

self-hosted - Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept

scikit-learn - scikit-learn: machine learning in Python

Keras - Deep Learning for humans

tensorflow - An Open Source Machine Learning Framework for Everyone

bodywork - ML pipeline orchestration and model deployments on Kubernetes.

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

TFLearn - Deep learning library featuring a higher-level API for TensorFlow.

MLflow - Open source platform for the machine learning lifecycle

gym - A toolkit for developing and comparing reinforcement learning algorithms.

Pylearn2 - Warning: This project does not have any current developer. See bellow.

Metrics - Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.