Predictive Data Science with Amazon SageMaker and a Data Lake on AWS
This Quick Start builds a data lake environment for building, training, and deploying machine learning (ML) models with Amazon SageMaker on the Amazon Web Services (AWS) Cloud. The deployment, which takes about 10-15 minutes, uses AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon API Gateway, AWS Lambda, Amazon Kinesis Data Streams, and Amazon Kinesis Data Firehose.
Amazon SageMaker is a managed platform that enables developers and data scientists to build, train, and deploy ML models quickly and easily.
This Quick Start is for users who want to unleash the power of their data to make predictive and prescriptive models for business value, without needing to configure complex ML hardware clusters. It enables end-to-end data science, starting with raw data and ending with a prediction REST API in a production system.
The Quick Start also provides a demo scenario developed by Pariveda Solutions. The demo shows how to store raw data in Amazon S3, transform the data for consumption in Amazon SageMaker, use Amazon SageMaker to build an ML model, and host the model in a prediction API for Amazon Elastic Compute Cloud (Amazon EC2) Spot pricing.
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