Deploy Amazon SageMaker and a Data Lake on AWS for Predictive Data Science with New Quick Start
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 takes about 10-15 minutes and uses AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon API Gateway, Amazon Kinesis Data Streams, and Amazon Kinesis Data Firehose.
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 takes about 10-15 minutes and uses AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon API Gateway, Amazon Kinesis Data Streams, and Amazon Kinesis Data Firehose.
Amazon SageMaker is a managed platform for developers and data scientists to build, train, and deploy ML models quickly and easily.
This Quick Start enables end-to-end data science for making predictive and prescriptive models, without having to configure complex ML hardware clusters.
The Quick Start provides a demo from Pariveda Solutions. It shows how to store raw data in Amazon S3, transform it for consumption in Amazon SageMaker, use Amazon SageMaker to build a model, and host the model in a prediction API for Amazon Elastic Compute Cloud (Amazon EC2) Spot pricing.
To get started:
- View the architecture and details
- View the deployment guide for instructions
- Download the AWS CloudFormation templates that automate the deployment
For more AWS Quick Start reference deployments, see our catalog.
Quick Starts are automated reference deployments that use AWS CloudFormation templates to deploy key technologies on AWS, following AWS best practices. This Quick Start was built in collaboration with Pariveda Solutions, Inc.