Amazon SageMaker vs Google Cloud Vision API

Amazon SageMaker
Amazon SageMaker

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Google Cloud Vision API
Google Cloud Vision API

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Amazon SageMaker vs Google Cloud Vision API: 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; Google Cloud Vision API: Understand the content of an image by encapsulating powerful machine learning models. Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API.

Amazon SageMaker and Google Cloud Vision API are primarily classified as "Machine Learning as a Service" and "Image Analysis API" tools respectively.

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, Google Cloud Vision API provides the following key features:

  • Powerful Image Analysis
  • Insight From Your Images
  • Detect Inappropriate Content

According to the StackShare community, Google Cloud Vision API has a broader approval, being mentioned in 25 company stacks & 8 developers stacks; compared to Amazon SageMaker, which is listed in 12 company stacks and 6 developer stacks.

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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 Google Cloud Vision API?

Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API.

Want advice about which of these to choose?Ask the StackShare community!

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        What are some alternatives to Amazon SageMaker and Google Cloud Vision API?
        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.
        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.
        FloydHub
        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.
        Algorithms.io
        Build And Run Predictive Applications For Streaming Data From Applications, Devices, Machines and Wearables
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