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MLPerf VS Datmo

Compare MLPerf VS Datmo and see what are their differences

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MLPerf logo MLPerf

Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.

Datmo logo Datmo

Datmo tools help power up your existing model workflow. A new standard built by data scientists, for data scientists.
  • MLPerf Landing page
    Landing page //
    2023-08-18
  • Datmo Landing page
    Landing page //
    2023-02-11

MLPerf videos

+ Add

SC22: AI Benchmarking & MLPerf™ Webinar

More videos:

  • Review - MLPerf & PyTorch | PyTorch Developer Day 2020
  • Review - Peter Mattson - MLPerf: Driving Innovation by Measuring Performance

Datmo videos

No Datmo videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to MLPerf and Datmo)

MLPerf

Datmo

Data Science And Machine Learning
42 42%
Data Science And Machine Learning
58% 58
Data Science Notebooks
44 44%
Data Science Notebooks
56% 56
Machine Learning Tools
35 35%
Machine Learning Tools
65% 65
Predictive Analytics
100 100%
Predictive Analytics
0% 0

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What are some alternatives?

When comparing MLPerf and Datmo, 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.

MCenter - Machine Learning Operationalization

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

Spell - Deep Learning and AI accessible to everyone

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.

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

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