Alternatives to GraphLab Create logo

Alternatives to GraphLab Create

scikit-learn, TensorFlow, Turi Create, Azure Machine Learning, and Amazon SageMaker are the most popular alternatives and competitors to GraphLab Create.
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What is GraphLab Create and what are its top alternatives?

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.
GraphLab Create is a tool in the Machine Learning as a Service category of a tech stack.

Top Alternatives to GraphLab Create

  • 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. ...

  • 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. ...

  • Turi Create

    Turi Create

    It simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app. ...

  • Azure Machine Learning

    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 SageMaker

    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. ...

  • Amazon Machine Learning

    Amazon Machine Learning

    This new AWS service helps you to use all of that data you鈥檝e 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鈥檛 have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure. ...

  • Algorithms.io

    Algorithms.io

    Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables ...

  • Amazon Elastic Inference

    Amazon Elastic Inference

    Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon. ...

GraphLab Create alternatives & related posts

scikit-learn logo

scikit-learn

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Easy-to-use and general-purpose machine learning in Python
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PROS OF SCIKIT-LEARN
  • 18
    Scientific computing
  • 14
    Easy
CONS OF SCIKIT-LEARN
  • 1
    Limited

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TensorFlow logo

TensorFlow

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Open Source Software Library for Machine Intelligence
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PROS OF TENSORFLOW
  • 24
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful
CONS OF TENSORFLOW
  • 8
    Hard
  • 5
    Hard to debug
  • 1
    Documentation not very helpful

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

See more
Turi Create logo

Turi Create

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A Machine Learning framework that simplifies the development of custom machine learning models ( By Apple )
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PROS OF TURI CREATE
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    CONS OF TURI CREATE
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      related Turi Create posts

      Azure Machine Learning logo

      Azure Machine Learning

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      A fully-managed cloud service for predictive analytics
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      PROS OF AZURE MACHINE LEARNING
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        CONS OF AZURE MACHINE LEARNING
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          Amazon SageMaker logo

          Amazon SageMaker

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          Accelerated Machine Learning
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          PROS OF AMAZON SAGEMAKER
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            CONS OF AMAZON SAGEMAKER
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              Julien DeFrance
              Principal Software Engineer at Tophatter | 2 upvotes 路 46.2K views

              Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

              Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

              Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

              Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

              See more

              Amazon SageMaker constricts the use of their own mxnet package and does not offer a strong Kubernetes backbone. At the same time, Kubeflow is still quite buggy and cumbersome to use. Which tool is a better pick for MLOps pipelines (both from the perspective of scalability and depth)?

              See more
              Amazon Machine Learning logo

              Amazon Machine Learning

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              Visualization tools and wizards that guide you through the process of creating ML models w/o having to learn...
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              PROS OF AMAZON MACHINE LEARNING
                Be the first to leave a pro
                CONS OF AMAZON MACHINE LEARNING
                  Be the first to leave a con

                  related Amazon Machine Learning posts

                  Julien DeFrance
                  Principal Software Engineer at Tophatter | 2 upvotes 路 46.2K views

                  Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.

                  Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.

                  Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.

                  Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.

                  See more
                  Algorithms.io logo

                  Algorithms.io

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                  Machine learning as a service for streaming data from connected devices.
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                  PROS OF ALGORITHMS.IO
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                    CONS OF ALGORITHMS.IO
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                      Amazon Elastic Inference logo

                      Amazon Elastic Inference

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                      GPU-Powered Deep Learning Inference Acceleration
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                      PROS OF AMAZON ELASTIC INFERENCE
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                        CONS OF AMAZON ELASTIC INFERENCE
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