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GraphLab Create
GraphLab Create

3
7
+ 1
3
Lateral
Lateral

2
10
+ 1
0
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GraphLab Create vs Lateral: What are the differences?

Developers describe GraphLab Create as "Machine learning platform that enables data scientists and app developers to easily create intelligent apps at scale". Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful. On the other hand, Lateral is detailed as "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.

GraphLab Create and Lateral belong to "Machine Learning as a Service" category of the tech stack.

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What is GraphLab Create?

Building an intelligent, predictive application involves iterating over multiple steps: cleaning the data, developing features, training a model, and creating and maintaining a predictive service. GraphLab Create does all of this in one platform. It is easy to use, fast, and powerful.

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.
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                What are some alternatives to GraphLab Create and Lateral?
                scikit-learn
                scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
                TensorFlow
                TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
                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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