Software Alternatives & Reviews

Weights & Biases VS Datatron

Compare Weights & Biases VS Datatron and see what are their differences

Weights & Biases logo Weights & Biases

Developer tools for deep learning research

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
  • Weights & Biases Landing page
    Landing page //
    2023-07-24
  • Datatron Landing page
    Landing page //
    2023-02-11

Weights & Biases videos

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Datatron videos

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

More videos:

  • Review - Virtual Records Management from Datatron

Category Popularity

0-100% (relative to Weights & Biases and Datatron)
Data Science And Machine Learning
Machine Learning Tools
0 0%
100% 100
Data Science Notebooks
52 52%
48% 48
Developer Tools
100 100%
0% 0

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What are some alternatives?

When comparing Weights & Biases and Datatron, you can also consider the following products

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.

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.

MCenter - Machine Learning Operationalization

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.

Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.

5Analytics - The 5Analytics AI platform enables you to use artificial intelligence to automate important commercial decisions and implement digital business models.