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Amazon SageMaker
Amazon SageMaker

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BigML
BigML

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Amazon SageMaker vs BigML: What are the differences?

Amazon SageMaker: Accelerated Machine Learning. A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale; 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.

Amazon SageMaker and BigML can be primarily classified as "Machine Learning as a Service" tools.

Some of the features offered by Amazon SageMaker are:

  • Build: managed notebooks for authoring models, built-in high-performance algorithms, broad framework support
  • Train: one-click training, authentic model tuning
  • Deploy: one-click deployment, automatic A/B testing, fully-managed hosting with auto-scaling

On the other hand, BigML provides the following key features:

  • 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
- No public GitHub repository available -
- No public GitHub repository available -

What is 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.

What is BigML?

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.
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          What are some alternatives to Amazon SageMaker and BigML?
          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.
          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.
          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.
          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.
          Algorithms.io
          Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
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          Decisions about Amazon SageMaker and BigML
          Julien DeFrance
          Julien DeFrance
          Principal Software Engineer at Tophatter | 2 upvotes 14K views
          atSmartZipSmartZip
          Amazon SageMaker
          Amazon SageMaker
          Amazon Machine Learning
          Amazon Machine Learning
          AWS Lambda
          AWS Lambda
          Serverless
          Serverless
          #FaaS
          #GCP
          #PaaS

          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.

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