Migrate ML Build, Train, and Deploy workloads to Amazon SageMaker using AWS Developer Tools
Created by Scot Marvin (AWS)
R Type: Replatform | Source: Machine Learning | Target: Amazon SageMaker |
Created by: AWS | Environment: PoC or pilot | Technologies: Machine learning & AI; DevOps; Migration |
AWS services: Amazon SageMaker |
Summary
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 and limitations
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
Architecture
Source technology stack
On-premises Linux compute instance with data on either the local file system or in a relational database
Source architecture
Target technology stack
AWS CodePipeline deployed with Amazon S3 for data storage and Amazon DynamoDB as metadata store for tracking or logging pipeline runs
Target architecture
Application migration architecture
Native Python package and AWS CodeCommit repository (and an SQL client, for on-premises datasets on database instance)
Tools
Python
Git
AWS CLI – The AWS CLI
deploys the AWS CloudFormation stack and moves data to the S3 bucket. The S3 bucket, in turn, leads to the target.
Epics
| Task | Description | Skills required |
|---|---|---|
| Validate source code and datasets. | Data scientist | |
| Identify target build, train, and deployment instance types and sizes. | Data engineer, Data scientist | |
| Create capability list and capacity requirements. | ||
| Identify network requirements. | DBA, Systems administrator | |
| Identify the network or host access security requirements for the source and target applications. | Data engineer, ML engineer, Systems administrator | |
| Determine backup strategy. | ML engineer, Systems administrator | |
| Determine availability requirements. | ML engineer, Systems administrator | |
| Identify the application migration or switchover strategy. | Data scientist, ML engineer |
| Task | Description | Skills required |
|---|---|---|
| Create a virtual private cloud (VPC). | ML engineer, Systems administrator | |
| Create security groups. | ML engineer, Systems administrator | |
| Set up an Amazon S3 bucket and AWS CodeCommit repository branches for ML code. | ML engineer |
| Task | Description | Skills required |
|---|---|---|
| Use native MySQL tools or third-party tools to migrate train, validate, and test datasets to provisioned S3 bucket. | This is required for AWS CloudFormation stack deployment. | Data engineer, ML engineer |
| Package the ML train and hosting code as Python packages and push to the provisioned repository in AWS CodeCommit or GitHub. | You need the repository's branch name to deploy the AWS CloudFormation template for migration. | Data scientist, ML engineer |
| Task | Description | Skills required |
|---|---|---|
| Follow the ML workload migration strategy. | Application owner, ML engineer | |
| Deploy the AWS CloudFormation stack. | Use the AWS CLI to create the stack declared in the YAML template provided with this solution. | Data scientist, ML engineer |
| Task | Description | Skills required |
|---|---|---|
| Switch the application clients over to the new infrastructure. | Application owner, Data scientist, ML engineer |
| Task | Description | Skills required |
|---|---|---|
| Shut down the temporary AWS resources. | Shut down any custom resources from the AWS CloudFormation template (for example, any AWS Lambda functions that aren't being used). | Data scientist, ML engineer |
| Review and validate the project documents. | Application owner, Data scientist | |
| Validate the results and the ML model evaluation metrics with operators. | Make sure that model performance matches the application users' expectations and is comparable to the on-premises state. | Application owner, Data scientist |
| Close out the project and provide feedback. | Application owner, ML engineer |
Related resources
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