Alternatives to FloydHub logo

Alternatives to FloydHub

Paperspace, Crystal, Azure Machine Learning, Amazon SageMaker, and Amazon Machine Learning are the most popular alternatives and competitors to FloydHub.
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What is FloydHub and what are its top alternatives?

Platform-as-a-Service for training and deploying your DL models in the cloud. Start running your first project in < 30 sec! Floyd takes care of the grunt work so you can focus on the core of your problem.
FloydHub is a tool in the Machine Learning as a Service category of a tech stack.

Top Alternatives to FloydHub

  • Paperspace

    Paperspace

    It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines. ...

  • Crystal

    Crystal

    Crystal is a programming language that resembles Ruby but compiles to native code and tries to be much more efficient, at the cost of disallowing certain dynamic aspects of Ruby. ...

  • 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. ...

  • Google AI Platform

    Google AI Platform

    Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. ...

FloydHub alternatives & related posts

Paperspace logo

Paperspace

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The way to access and manage limitless computing power in the cloud
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PROS OF PAPERSPACE
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    CONS OF PAPERSPACE
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      Crystal logo

      Crystal

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      Fast as C, slick as Ruby
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      PROS OF CRYSTAL
      • 36
        Compiles to efficient native code
      • 33
        Ruby inspired syntax
      • 30
        Performance oriented - C-like speeds
      • 22
        Gem-like packages, called Shards
      • 18
        Can call C code using Crystal bindings
      • 18
        Typed Ruby <3
      • 16
        Super Fast
      • 15
        Open Source
      • 13
        Minimal Runtime
      • 10
        Cute
      • 7
        Clean code
      • 7
        Concurrent
      • 7
        Productive
      • 3
        Great community
      • 2
        Simplicity
      • 2
        Program compiled into a single binary
      • 2
        Powerful
      • 1
        Elegant
      • 1
        Has builtin LLVM support library
      • 1
        Feels like duck types, safe like static types
      • 1
        Statically linked binaries that are simple to deploy
      • 1
        Fun to write
      • 1
        High-performance
      • 1
        Simple, minimal syntax
      • 1
        Compile time statically safe macros
      • 1
        Null Safety
      • 1
        Concise
      • 1
        Statically Safe Monkey Patching
      • 1
        Fibers
      • 1
        Spawn
      • 1
        Meta-programming
      • 1
        Type inference
      • 1
        Cross-platform
      • 1
        Productivity
      • 1
        Meta-Programming (via Macros)
      CONS OF CRYSTAL
      • 12
        Small community
      • 3
        No windows support
      • 1
        No Oracle lib

      related Crystal 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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                  + 1
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                  PROS OF ALGORITHMS.IO
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                    CONS OF ALGORITHMS.IO
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                      related Algorithms.io posts

                      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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                          Google AI Platform logo

                          Google AI Platform

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                          Create your AI applications once, then run them easily on both GCP and on-premises
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                          PROS OF GOOGLE AI PLATFORM
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                            CONS OF GOOGLE AI PLATFORM
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