Alternatives to Algorithms.io logo

Alternatives to Algorithms.io

Amazon SageMaker, Azure Machine Learning, Amazon Machine Learning, Amazon Elastic Inference, and Google AI Platform are the most popular alternatives and competitors to Algorithms.io.
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What is Algorithms.io and what are its top alternatives?

Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
Algorithms.io is a tool in the Machine Learning as a Service category of a tech stack.

Top Alternatives to Algorithms.io

  • 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. ...

  • 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 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 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. ...

  • Amazon Personalize

    Amazon Personalize

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

  • 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. ...

  • Firebase Predictions

    Firebase Predictions

    Firebase Predictions uses the power of Google’s machine learning to create dynamic user groups based on users’ predicted behavior. ...

Algorithms.io alternatives & related posts

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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      related Amazon SageMaker posts

      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
      Julien DeFrance
      Principal Software Engineer at Tophatter · | 2 upvotes · 50.3K 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
      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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          related Azure Machine Learning posts

          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
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            CONS OF AMAZON MACHINE LEARNING
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              related Amazon Machine Learning posts

              Julien DeFrance
              Principal Software Engineer at Tophatter · | 2 upvotes · 50.3K 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 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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                  related Amazon Elastic Inference posts

                  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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                      related Google AI Platform posts

                      Amazon Personalize logo

                      Amazon Personalize

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                      Real-time personalization and recommendation
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                      PROS OF AMAZON PERSONALIZE
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                        CONS OF AMAZON PERSONALIZE
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                          related Amazon Personalize posts

                          NanoNets logo

                          NanoNets

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                          Machine learning API with less data
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                          PROS OF NANONETS
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                            Simple API
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                            Easy Setup
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                            Easy to use
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                            Fast Training
                          CONS OF NANONETS
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                            Firebase Predictions logo

                            Firebase Predictions

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                            Define dynamic user groups based on predicted behavior
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                            PROS OF FIREBASE PREDICTIONS
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                                related Firebase Predictions posts