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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.
Amazon SageMaker is a tool in the Machine Learning as a Service category of a tech stack.

Who uses Amazon SageMaker?

Companies
29 companies reportedly use Amazon SageMaker in their tech stacks, including Zola, SoFi, and Catho.

Developers
24 developers on StackShare have stated that they use Amazon SageMaker.

Amazon SageMaker Integrations

Why developers like Amazon SageMaker?

Here鈥檚 a list of reasons why companies and developers use Amazon SageMaker
Top Reasons
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Amazon SageMaker Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Amazon SageMaker in their tech stack.

Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter | 2 upvotes 16.9K 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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Amazon SageMaker's Features

  • 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

Amazon SageMaker Alternatives & Comparisons

What are some alternatives to Amazon SageMaker?
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
See all alternatives

Amazon SageMaker's Followers
26 developers follow Amazon SageMaker to keep up with related blogs and decisions.
Shota Saitoh
Kip P.
Vishal Kumar Singh
John Alton
soulnai2896
JahnKhan
Ryan Hicks
Simon Rascovsky
Arun Kumar
Krishnan Subramanian