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Lateral
Lateral

2
10
+ 1
0
NanoNets
NanoNets

12
31
+ 1
15
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Lateral vs NanoNets: What are the differences?

Lateral: The ultimate API for recommending your content. Delight your users with personalised content recommendations. It's easy to set up and works with or without collaborative data. The Lateral API is trained on 10s of millions of high quality documents from law, academia and journalism. It can understand any document and provide intelligent recommendations; NanoNets: 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.

Lateral and NanoNets can be categorized as "Machine Learning as a Service" tools.

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- No public GitHub repository available -

What is Lateral?

Delight your users with personalised content recommendations. It's easy to set up and works with or without collaborative data. The Lateral API is trained on 10s of millions of high quality documents from law, academia and journalism. It can understand any document and provide intelligent recommendations.

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.
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            What are some alternatives to Lateral and NanoNets?
            Buffer
            Buffer helps you manage multiple social media accounts at once. Quickly schedule content from anywhere on the web, collaborate with team members, and analyze rich statistics on how your posts perform.
            Hootsuite
            It manages all your social media in one place. From finding prospects to serving customers, it helps you do more with your social media. The system's user interface takes the form of a dashboard, and supports social network integrations.
            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.
            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.
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