Getting Started With Machine Learning On AWS Sagemaker

 

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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 and edge-devices.

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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.

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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, where developers can create their own ML algorithms from scratch.

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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, AWS Batch for offline batch processing, or Amazon Kinesis for real-time processing.

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REFERENCE:

https://en.wikipedia.org/wiki/Amazon_SageMaker

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TUTORIAL:

In this tutorial, you will assume the role of a machine learning developer working at a bank. You have been asked to develop a machine learning model to predict whether a customer will enroll for a certificate of deposit (CD).


In this tutorial, you learn how to:

• Create a SageMaker notebook instance
• Prepare the data
• Train the model to learn from the data
• Deploy the model
• Evaluate your ML model's performance

https://aws.amazon.com/getting-started/hands-on/build-train-deploy-machine-learning-model-sagemaker/

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