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Gradient° vs NanoNets

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Gradient°
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16
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NanoNets
17
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+ 1
19
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Gradient° vs NanoNets: What are the differences?

Developers describe Gradient° as "Deep learning platform built for developers". Gradient° is a suite of tools for exploring data and training neural networks. Gradient° includes 1-click Jupyter notebooks, a powerful job runner, and a python module to run any code on a fully managed GPU cluster in the cloud. Gradient is also rolling out full support for Google's new TPUv2 accelerator to power even more newer workflows. On the other hand, NanoNets is detailed as "Machine learning API with less data". Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

Gradient° and NanoNets belong to "Machine Learning as a Service" category of the tech stack.

Some of the features offered by Gradient° are:

  • 1-click Jupyter notebooks
  • a powerful job runner
  • Python module to run any code on a fully managed GPU cluster in the cloud

On the other hand, NanoNets provides the following key features:

  • Image categorization API with less than 30 images per category
  • Custom object localization API
  • Text deduplication API
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Pros of Gradient°
Pros of NanoNets
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    What is Gradient°?

    Gradient° is a suite of tools for exploring data and training neural networks. Gradient° includes 1-click Jupyter notebooks, a powerful job runner, and a python module to run any code on a fully managed GPU cluster in the cloud. Gradient is also rolling out full support for Google's new TPUv2 accelerator to power even more newer workflows.

    What is NanoNets?

    Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

    Need advice about which tool to choose?Ask the StackShare community!

    Jobs that mention Gradient° and NanoNets as a desired skillset
    Sr. Staff Machine Learning Engineer, Ads Content Integrity & Quality
    Pinterest
    San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
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    What companies use Gradient°?
    What companies use NanoNets?
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      What tools integrate with Gradient°?
      What tools integrate with NanoNets?

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