BigML vs GraphLab Create

Overview

BigML

Stacks14

Followers29

Votes1

GraphLab Create

Stacks8

Followers40

Votes3

BigML vs GraphLab Create: What are the differences?

BigML: Machine Learning, made simple. Predictive analytics for big data and not-so-big data. BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services; GraphLab Create: 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.

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

Some of the features offered by BigML are:

REST API
bindings in Pyton, Java, Ruby, node.js, C#, Clojure, PHP, and more
several algorithms, including categorical & regression decision trees, ensembles of trees (random decision forest), cluster analysis and more

On the other hand, GraphLab Create provides the following key features:

Analyze terabyte scale data at interactive speeds, on your desktop.
A Single platform for tabular data, graphs, text, and images.
State of the art machine learning algorithms including deep learning, boosted trees, and factorization machines.

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Detailed Comparison

BigML	GraphLab Create
BigML provides a hosted machine learning platform for advanced analytics. Through BigML's intuitive interface and/or its open API and bindings in several languages, analysts, data scientists and developers alike can quickly build fully actionable predictive models and clusters that can easily be incorporated into related applications and services.	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.
REST API; bindings in Pyton, Java, Ruby, node.js, C#, Clojure, PHP, and more; several algorithms, including categorical & regression decision trees, ensembles of trees (random decision forest), cluster analysis and more; models are fully actionable -- translated into code that can be cut/paste for local utilization; PredictServer (and Amazon AMI) can be used for real-time or large batch predictions; models can be shared privately or publicly (for free or for a fee set by the developer)	Analyze terabyte scale data at interactive speeds, on your desktop.;A Single platform for tabular data, graphs, text, and images.;State of the art machine learning algorithms including deep learning, boosted trees, and factorization machines.;Run the same code on your laptop or in a distributed system, using a Hadoop Yarn or EC2 cluster.;Focus on tasks or machine learning with the flexible API.;Easily deploy data products in the cloud using Predictive Services.;Visualize data for exploration and production monitoring.
Statistics
Stacks 14	Stacks 8
Followers 29	Followers 40
Votes 1	Votes 3
Pros & Cons
Pros 1 Ease of use, great REST API and ML workflow automation	Pros 1 Intelligent Function Defaults 1 Fast Data Summary 1 Simple Machine Learning Tools

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