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

Gradient°

4
16
+ 1
0
NanoNets

17
47
+ 1
19
Add tool

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
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Gradient°
Pros of NanoNets
    Be the first to leave a pro
    • 7
      Simple API
    • 5
      Easy Setup
    • 4
      Easy to use
    • 3
      Fast Training

    Sign up to add or upvote prosMake informed product decisions

    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
    What companies use Gradient°?
    What companies use NanoNets?
      No companies found
      See which teams inside your own company are using Gradient° or NanoNets.
      Sign up for StackShare EnterpriseLearn More

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with Gradient°?
      What tools integrate with NanoNets?

      Sign up to get full access to all the tool integrationsMake informed product decisions

      What are some alternatives to Gradient° and NanoNets?
      Amazon SageMaker
      A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.
      Azure Machine Learning
      Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
      Amazon Machine Learning
      This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure.
      Algorithms.io
      Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
      Google AI Platform
      Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively.
      See all alternatives