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]
2018-11-08: 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]
