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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
46 companies reportedly use Amazon SageMaker in their tech stacks, including lido, TransferWise, and QuintoAndar.

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

Amazon SageMaker Integrations

Amazon EC2, TensorFlow, Amazon Elastic Inference, Caffe, and AWS DeepLens are some of the popular tools that integrate with Amazon SageMaker. Here's a list of all 5 tools that integrate with Amazon SageMaker.
Public Decisions about Amazon SageMaker

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

Arthur Boghossian
Arthur Boghossian
DevOps Engineer at PlayAsYouGo · | 3 upvotes · 3.6K views

For our Compute services, we decided to use AWS Lambda as it is perfect for quick executions (perfect for a bot), is serverless, and is required by Amazon Lex, which we will use as the framework for our bot. We chose Amazon Lex as it integrates well with other #AWS services and uses the same technology as Alexa. This will give customers the ability to purchase licenses through their Alexa device. We chose Amazon DynamoDB to store customer information as it is a noSQL database, has high performance, and highly available. If we decide to train our own models for license recommendation we will either use Amazon SageMaker or Amazon EC2 with AWS Elastic Load Balancing (ELB) and AWS ASG as they are ideal for model training and inference.

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Julien DeFrance
Julien DeFrance
Principal Software Engineer at Tophatter · | 2 upvotes · 37.8K 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.

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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’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.
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
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.
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
See all alternatives

Amazon SageMaker's Followers
153 developers follow Amazon SageMaker to keep up with related blogs and decisions.
Philip Reilly
Kiran Narayanaswamy
Anmol  Gulwani
Parag Kadwe
Yu Gao
Luke Qin
Nimish Sanghi
Chen Sheng Wang
Richard Hurley
明明 张