Posted On: Aug 15, 2018
This Quick Start builds a data lake environment for building, training, and deploying machine learning (ML) models with HAQM SageMaker on the HAQM Web Services (AWS) Cloud. The deployment takes about 10-15 minutes and uses AWS services such as HAQM Simple Storage Service (HAQM S3), HAQM API Gateway, HAQM Kinesis Data Streams, and HAQM Kinesis Data Firehose.
HAQM 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 HAQM S3, transform it for consumption in HAQM SageMaker, use HAQM SageMaker to build a model, and host the model in a prediction API for HAQM Elastic Compute Cloud (HAQM 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.