This pattern provides guidance for migrating an on-premises machine learning (ML) application running on Unix or Linux servers to be trained and deployed on AWS using Amazon SageMaker. This deployment uses a continuous integration and continuous deployment (CI/CD) pipeline. The migration pattern is deployed using an AWS CloudFormation stack.
Prerequisites
An active AWS account using AWS Landing Zone
AWS Command Line Interface (AWS CLI) installed and configured on your Unix or Linux server
An ML source code repository in either GitHub, AWS CodeCommit, or Amazon Simple Storage Service (Amazon S3)
Limitations
Only 300 individual pipelines can be deployed in one AWS Region.
This pattern is intended for supervised ML workloads with train-and-deploy code in Python.
Product versions
Docker version 19.03.5, build 633a0ea, using Python 3.6x
Source technology stack
On-premises Linux compute instance with data on either the local file system or in a relational database
Source architecture
