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  1. Stackups
  2. AI
  3. Text & Language Models
  4. Machine Learning As A Service
  5. Amazon Personalize vs GraphLab Create

Amazon Personalize vs GraphLab Create

OverviewComparisonAlternatives

Overview

GraphLab Create
GraphLab Create
Stacks8
Followers40
Votes3
Amazon Personalize
Amazon Personalize
Stacks20
Followers62
Votes0

Amazon Personalize vs GraphLab Create: What are the differences?

Amazon Personalize: Real-time personalization and recommendation. Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications; 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.

Amazon Personalize and GraphLab Create can be categorized as "Machine Learning as a Service" tools.

Some of the features offered by Amazon Personalize are:

  • Combine customer and contextual data to generate high-quality recommendations
  • Automated machine learning
  • Continuous learning to improve performance

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

GraphLab Create
GraphLab Create
Amazon Personalize
Amazon Personalize

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.

Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.

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.
Combine customer and contextual data to generate high-quality recommendations; Automated machine learning; Continuous learning to improve performance; Bring your own algorithms; Easily integrate with your existing tools;
Statistics
Stacks
8
Stacks
20
Followers
40
Followers
62
Votes
3
Votes
0
Pros & Cons
Pros
  • 1
    Intelligent Function Defaults
  • 1
    Fast Data Summary
  • 1
    Simple Machine Learning Tools
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What are some alternatives to GraphLab Create, Amazon Personalize?

TensorFlow

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.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

NanoNets

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.

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

Inferrd

Inferrd

It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.

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