Amazon SageMaker

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Amazon SageMaker
Developer(s) Amazon, Amazon Web Services
Initial release 29 November 2017; 7 years ago (2017-11-29)
Type Software as a service
Website aws.amazon.com/sagemaker

Amazon SageMaker is a cloud machine-learning platform that was launched in November 2017.[1] SageMaker enables developers to create, train, and deploy machine-learning (ML) models in the cloud.[2] SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.[3][4]

Capabilities

SageMaker enables developers to operate at a number of levels of abstraction when training and deploying machine learning models. At its highest level of abstraction, SageMaker provides pre-trained ML models that can be deployed as-is.[5] In addition, SageMaker provides a number of built-in ML algorithms that developers can train on their own data.[6][7] Further, SageMaker provides managed instances of TensorFlow and Apache MXNet, where developers can create their own ML algorithms from scratch.[8] Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other AWS services, such as the Amazon DynamoDB database for structured data storage,[9] AWS Batch for offline batch processing,[9][10] or Amazon Kinesis for real-time processing.[11]

Development interfaces

A number of interfaces are available for developers to interact with SageMaker. First, there is a web API that remotely controls a SageMaker server instance.[12] While the web API is agnostic to the programming language used by the developer, Amazon provides SageMaker API bindings for a number of languages, including Python, JavaScript, Ruby, Java, and Go.[13][14] In addition, SageMaker provides managed Jupyter Notebook instances for interactively programming SageMaker and other applications.[15][16]

History and features

  • 2017-11-29: SageMaker is launched at the AWS re:Invent conference.[1][6][2]
  • 2018-02-27: Managed TensorFlow and MXNet deep neural network training and inference are now supported within SageMaker.[17][8]
  • 2018-02-28: SageMaker automatically scales model inference to multiple server instances.[18][19]
  • 2018-07-13: SageMaker adds support for recurrent neural network training, word2vec training, multi-class linear learner training, and distributed deep neural network training in Chainer with Layer-wise Adaptive Rate Scaling (LARS).[20][7]
  • 2018-07-17: AWS Batch Transform enables high-throughput non-realtime machine learning inference in SageMaker.[21][22]
  • 2018-11-08: Support for training and inference of Object2Vec word embeddings.[23][24]
  • 2018-11-27: SageMaker Ground Truth "makes it much easier for developers to label their data using human annotators through Mechanical Turk, third-party vendors, or their own employees."[25][3]
  • 2018-11-28: SageMaker Reinforcement Learning (RL) "enables developers and data scientists to quickly and easily develop reinforcement learning models at scale."[26][3]
  • 2018-11-28: SageMaker Neo enables deep neural network models to be deployed from SageMaker to edge-devices such as smartphones and smart cameras.[27][3]
  • 2018-11-29: The AWS Marketplace for SageMaker is launched. The AWS Marketplace enables 3rd-party developers to buy and sell machine learning models that can be trained and deployed in SageMaker.[28]
  • 2019-01-27: SageMaker Neo is released as open-source software.[29]

Uses

  • NASCAR is using SageMaker to train deep neural networks on 70 years of video data.[30]
  • Carsales.com uses SageMaker to train and deploy machine learning models to analyze and approve automotive classified ad listings.[31]
  • Avis Budget Group and Slalom Consulting are using SageMaker to develop "a practical on-site solution that could address the over- and under-utilization of cars in real-time using an optimization engine built in Amazon SageMaker."[32]
  • Volkswagen Group uses SageMaker to develop and deploy machine learning in its manufacturing plants.[33]
  • Peak and Footasylum use SageMaker in a recommendation engine for footwear.[34]

Favorable articles on SageMaker

In 2019, CIOL named SageMaker one of the "5 Best Machine Learning Platforms For Developers," alongside IBM Watson, Microsoft Azure Machine Learning, Apache PredictionIO, and ai-one.[35]

See also

References

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