Amazon SageMaker

Amazon SageMaker AI
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 AI is a cloud-based machine-learning platform that allows the creation, training, and deployment by developers of machine-learning (ML) models on the cloud. ^[1] It can be used to deploy ML models on embedded systems and edge-devices. ^{[2] [3]} The platform was launched in November 2017. ^[4]

Capabilities

SageMaker enables developers to operate at a number of different 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, it offers a number of built-in ML algorithms that developers can train on their own data. ^{[6] [7]}

The platform also features 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. ^{[4] [6] [1]}
• 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: Support is added 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-real-time 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]
• 2018-11-28: SageMaker Reinforcement Learning (RL) "enables developers and data scientists to quickly and easily develop reinforcement learning models at scale." ^{[26] [2]}
• 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] [2]}
• 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]

Notable Customers

• 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]

Awards

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 AiONE. ^[35]

See also

• Amazon Web Services
• Amazon Lex
• Amazon Polly
• Amazon Rekognition
• Amazon Mechanical Turk
• Timeline of Amazon Web Services

References

1. Woodie, Alex (2017-11-29). "AWS Takes the 'Muck' Out of ML with SageMaker". datanami. Retrieved 2019-06-09.
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15. "Use Notebook Instances". AWS. Retrieved 2019-06-09.
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24. "Amazon SageMaker Now Supports Object2Vec and IP Insights Built-in Algorithms". AWS. 2018-11-19. Retrieved 2019-06-09.
25. "Introducing Amazon SageMaker Ground Truth - Build Highly Accurate Training Datasets Using Machine Learning". AWS. 2018-11-28. Retrieved 2019-06-09.
26. "Introducing Reinforcement Learning Support with Amazon SageMaker RL". AWS. 2018-11-28. Retrieved 2019-06-09.
27. "Introducing Amazon SageMaker Neo - Train Once, Run Anywhere with up to 2x in Performance Improvement". AWS. 2018-11-28. Retrieved 2019-06-09.
28. Robuck, Mike (2018-11-29). "AWS goes deep and wide with machine learning services and capabilities". FierceTelecom. Retrieved 2019-06-09.
29. Janakiram, MSV (2019-01-27). "Amazon Open Sources SageMaker Neo To Run Machine Learning Models At The Edge". Forbes. Retrieved 2019-06-09.
30. Digman, Larry (2019-06-04). "NASCAR to migrate 18 petabytes of video archives to AWS". ZDNet. Retrieved 2019-06-09.
31. Crozier, Ry (2019-05-02). "Carsales builds Tessa AI to check vehicle ads". IT News. Retrieved 2019-06-09.
32. "Avis Budget Group and Slalom Further Digitize the Car Rental Process with Machine Learning on AWS". AWS. 2019-05-31. Retrieved 2019-06-09.
33. "Volkswagen and AWS Join Forces to Transform Automotive Manufacturing". Metrology News. 2019-05-24. Archived from the original on 2020-10-28. Retrieved 2019-06-09.
34. Mari, Angelica (2019-05-14). "Footasylum steps up artificial intelligence to drive customer centricity". Computer Weekly. Retrieved 2019-06-09.
35. Pandey, Ashok (2019-02-21). "5 Best Machine Learning Platforms For Developers". CIOL. Retrieved 2019-06-09.