Amazon SageMaker is a cloud machine-learning platform that was launched in November 2017. SageMaker enables developers to create, train, and deploy machine-learning (ML) models in the cloud. SageMaker also enables developers to deploy ML models on

embedded systems An embedded system is a computer system—a combination of a computer processor, computer memory, and input/output peripheral devices—that has a dedicated function within a larger mechanical or electronic system. It is ''embedded'' ...

and edge-devices.

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. In addition, SageMaker provides a number of built-in ML algorithms that developers can train on their own data. Further, SageMaker provides managed instances of TensorFlow and

Apache MXNet Apache MXNet is an open-source deep learning software framework, used to train and deploy deep neural networks. It is scalable, allowing for fast model training and supports a flexible programming model and multiple programming languages (inclu ...

, where developers can create their own ML algorithms from scratch. Regardless of which level of abstraction is used, a developer can connect their SageMaker-enabled ML models to other

AWS Amazon Web Services, Inc. (AWS) is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis. These cloud computing web services provide di ...

services, such as the

Amazon DynamoDB Amazon DynamoDB is a fully managed proprietary NoSQL database service that supports key–value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. DynamoDB exposes a similar data model to and ...

database for structured data storage, AWS Batch for offline batch processing, or Amazon Kinesis for real-time processing.

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. 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,

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, and Go. In addition, SageMaker provides managed Jupyter Notebook instances for interactively programming SageMaker and other applications.

History and features

* 2017-11-29: SageMaker is launched at the AWS re:Invent conference. * 2018-02-27: Managed TensorFlow and

MXNet Apache MXNet is an open-source deep learning software framework, used to train and deploy deep neural networks. It is scalable, allowing for fast model training and supports a flexible programming model and multiple programming languages (inclu ...

deep neural network training and inference are now supported within SageMaker. *2018-02-28: SageMaker automatically scales model inference to multiple server instances. *2018-07-13: SageMaker adds support for

recurrent neural network A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes. This allows it to exhibit temporal dynamic ...

training, word2vec training, multi-class linear learner training, and distributed deep neural network training in Chainer with Layer-wise Adaptive Rate Scaling (LARS). *2018-07-17: AWS Batch Transform enables high-throughput non-realtime machine learning inference in SageMaker. *2018-11-08: Support for training and inference of Object2Vec word embeddings. *2018-11-27: SageMaker Ground Truth "makes it much easier for developers to label their data using human annotators through

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, third-party vendors, or their own employees." *2018-11-28: SageMaker

Reinforcement Learning Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...

(RL) "enables developers and data scientists to quickly and easily develop reinforcement learning models at scale." *2018-11-28: SageMaker Neo enables deep neural network models to be deployed from SageMaker to edge-devices such as smartphones and smart cameras. *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. *2019-01-27: SageMaker Neo is released as open-source software.

Uses

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is using SageMaker to train deep neural networks on 70 years of video data. *

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uses SageMaker to train and deploy machine learning models to analyze and approve automotive classified ad listings. * Avis Budget Group and

Slalom Consulting Slalom is a business and technology consulting firm headquartered in Seattle, Washington. The company, which is a division of Slalom, Inc, employs more than 13,000 people in 43 markets and 11 Build Centers across North America, Europe, and Asia Pa ...

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." *

Volkswagen Group Volkswagen AG (), known internationally as the Volkswagen Group, is a German multinational automotive manufacturer headquartered in Wolfsburg, Lower Saxony, Germany. The company designs, manufactures and distributes passenger and commercial ...

uses SageMaker to develop and deploy machine learning in its manufacturing plants. * Peak and Footasylum use SageMaker in a recommendation engine for footwear.

Favorable articles on SageMaker

In 2019, CIOL named SageMaker one of the "5 Best Machine Learning Platforms For Developers," alongside

IBM Watson IBM Watson is a question-answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's founder ...

, Microsoft Azure Machine Learning, Apache PredictionIO, and ai-one.

References