Amazon SageMaker vs Firebase Predictions

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Amazon SageMaker
Amazon SageMaker

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Firebase Predictions
Firebase Predictions

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Amazon SageMaker vs Firebase Predictions: What are the differences?

What is 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.

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

Amazon SageMaker and Firebase Predictions 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, Firebase Predictions provides the following key features:

  • Boost revenue and retention through customized user experiences
  • Send smarter notifications
  • Create custom predictions
- 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 Firebase Predictions?

Firebase Predictions uses the power of Google’s machine learning to create dynamic user groups based on users’ predicted behavior.
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            What tools integrate with Amazon SageMaker?
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            What are some alternatives to Amazon SageMaker and Firebase Predictions?
            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.
            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.
            Algorithms.io
            Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
            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.
            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.
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            Decisions about Amazon SageMaker and Firebase Predictions
            Julien DeFrance
            Julien DeFrance
            Principal Software Engineer at Tophatter · | 2 upvotes · 24.3K views
            atSmartZipSmartZip
            Serverless
            Serverless
            AWS Lambda
            AWS Lambda
            Amazon Machine Learning
            Amazon Machine Learning
            Amazon SageMaker
            Amazon SageMaker
            #PaaS
            #GCP
            #FaaS

            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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