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Datatron VS Comet.ml

Compare Datatron VS Comet.ml and see what are their differences

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Datatron logo Datatron

Datatron automates the deployment, monitoring, governance, and validation of your machine learning models in scikit-learn, TensorFlow, Keras, Pytorch, R, H20 and SAS

Comet.ml logo Comet.ml

Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.
  • Datatron Landing page
    Landing page //
    2023-02-11
  • Comet.ml Landing page
    Landing page //
    2023-09-16

Datatron videos

+ Add

Harish Doddi demos Datatron @SFNewTech on 1 Mar 2017 #SFNT @getdatatron

More videos:

  • Review - Virtual Records Management from Datatron

Comet.ml videos

+ Add

Running Effective Machine Learning Teams: Common Issues, Challenges & Solutions | Comet.ml

More videos:

  • Review - Comet.ml - Supercharging Machine Learning

Category Popularity

0-100% (relative to Datatron and Comet.ml)

Datatron

Comet.ml

Data Science And Machine Learning
42 42%
Data Science And Machine Learning
58% 58
Machine Learning Tools
62 62%
Machine Learning Tools
38% 38
Data Science Notebooks
48 48%
Data Science Notebooks
52% 52
Machine Learning
0 0%
Machine Learning
100% 100

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What are some alternatives?

When comparing Datatron and Comet.ml, you can also consider the following products

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

MCenter - Machine Learning Operationalization

Weights & Biases - Developer tools for deep learning research

Managed MLflow - Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.

Numericcal - Machine Learning Operationalization

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