This article contains content that is written like an advertisement .(September 2023) |
Developer(s) | Amazon, Amazon Web Services |
---|---|
Initial release | 29 November 2017 |
Type | Software as a service |
Website | aws |
Amazon SageMaker 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] SageMaker was launched in November 2017. [4]
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, 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]
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]
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]
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