Amazon SageMaker is a powerful tool that enables us to build, train, and deploy at scale our machine learning-based workloads. With help from AWS CI/CD tools, we can speed up this pipeline process. In this talk, we discuss how to integrate Amazon SageMaker into a CI/CD pipeline as well as how to orchestrate with other serverless components. This session is part of re:Invent Developer Community Day, a series led by AWS enthusiasts who share first-hand, technical insights on trending topics.

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CI/CD for Your Machine Learning
Pipeline with Amazon SageMaker
Vit Niennattrakul, Ph.D.
Managing Director, DailiTech, Thailand
AWS Community Hero, Thailand
D V C 3 0 3

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Vit Niennattrakul
- Ph.D. in data mining (time series)
- AWS Community Hero
- AWS User Group – Thailand
- Managing director at DailiTech
- AWS external instructor

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Agenda
CI/CD on Amazon Web Services
Machine learning with Amazon SageMaker
Architecture integrated with Amazon SageMaker
CI/CD on Amazon SageMaker

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Goal
Developers require a DevOps to make agility, reliability, and consistency
Data scientists require that DevOps as well

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Continuous integration/continuous delivery
https://aws.amazon.com/devops/continuous-integration/

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Why do we need CI/CD?
• Fix bug earlier and faster
• Deliver faster and often
• Unblock developers
• Grow skills factor

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CI/CD with AWS developer tools

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AWS CodePipeline
• Continuous delivery service for fast and reliable
application updates
• Model and visualize your software release process
• Builds, tests, and deploys your code every time
there is a code change
• Integrates with third-party tools and AWS

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AWS CodePipeline

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AWS CodePipeline

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AWS CodeCommit
• Secure, scalable, and managed Git source control
• Use standard Git tools
• Scalability, availability, and durability of Amazon
Simple Storage Service (Amazon S3)
• Encryption at rest with customer-specific keys
• No repo size limit
• Post commit hooks to call out to Amazon Simple
Notification Service (Amazon SNS)/AWS Lambda

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AWS CodeBuild
• Fully managed build service that compiles source
code, runs tests, and produces software packages
• Scales continuously and processes multiple
builds concurrently
• You can provide custom build environments
suited to your needs via Docker images
• Only pay by the minute for the compute
resources you use
• Launched with AWS CodePipeline and Jenkins
integration

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What is machine learning?
Machine learning is a field of artificial
intelligence that uses statistical techniques to
give computer systems the ability to "learn"
(for example, progressively improve
performance on a specific task) from data,
without being explicitly programmed
-Wikipedia

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Machine learning with Amazon Web Services

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Machine learning with Amazon SageMaker

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Machine learning with Amazon SageMaker

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Machine learning with Amazon SageMaker

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Build—Jupyter Notebook

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Build—Jupyter Notebook

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Train—Training jobs

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Tune—Hyperparameter tuning

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Inference—Model & endpoint configuration

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Amazon SageMaker AI/ML algorithms
Built-in algorithm
• Linear learner
• XGBoost algorithm
• Factorization machines
• K-means algorithm
• Principal component analysis (PCA)
• Image classification algorithm
• Sequence to sequence
• Latent Dirichlet allocation (LDA)
• Neural topic model (NTM)
Custom algorithm

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AI/ML integrated with business application
Business Application AI Service
Data in Data Lake

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AI/ML integrated with business application
Business Application AI Service Data Lake
Business Application
Client
Mobile client
AI Service Data Lake

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AI/ML-as-a-service building flow
Development
Environment
Raw Data

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AI/ML-as-a-service building flow
Endpoint Raw Data

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AI/ML-as-a-service building flow
Endpoint Raw DataApply some business
logics
Handle API key,
cache, etc.

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AI/ML-as-a-service building flow
Endpoint Raw DataApply some business
logics
Handle API key,
cache, etc.

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AI/ML-as-a-service building flow
Endpoint Raw DataApply some business
logics
Handle API key,
cache, etc.

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AI/ML-as-a-service building flow
Endpoint Raw DataApply some business
logics
Handle API key,
cache, etc.

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AI/ML-as-a-service building flow
Endpoint Raw DataApply some business
logics
Handle API key,
cache, etc.

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AI/ML-as-a-service building flow
Endpoint Raw DataApply some business
logics
Handle API key,
cache, etc.

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AI/ML-as-a-service building flow
Endpoint Raw DataApply some business
logics
Handle API key,
cache, etc.
Let’s demo

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Challenges
- Versioning control for Jupyter Notebook file
- Consistency on multiple environments (dev, test, production)
- Deployment to production environment may not be possible for data
scientist permission
- On-going build an incremental model

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Version control on Amazon SageMaker

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Version control on Amazon SageMaker

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Version control on Amazon SageMaker
Data Scientist
Data Scientist
Data Scientist
Data Scientist

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Amazon CodePipeline configuration

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Amazon CodePipeline configuration

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Automate deployment and execution

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Amazon CodePipeline configuration

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Amazon CodePipeline configuration
Raw dataInference modelApply some
business logics
Handle API key,
cache, etc.

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Amazon CodePipeline configuration

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Amazon CodePipeline configuration
Raw dataInference modelApply some
business logics
Handle API key,
cache, etc.

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Amazon CodePipeline configuration

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Amazon CodePipeline configuration
Raw dataInference modelApply some
business logics
Handle API key,
cache, etc.

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Amazon CodePipeline configuration
Raw dataInference modelApply some
business logics
Handle API key,
cache, etc.Let’s demo

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Benefits
- Can do versioning control for Jupyter Notebook file
- Can do consistency deployment on multiple environments (dev, test,
production)
- Can do deployment with the same code from development to
production environment
- Support on-going build an incremental model

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Conclusion
• CI/CD on Amazon Web Services has a full service from all stages of
development that are source, build, test, and production
• Machine learning with Amazon SageMaker is flexible and scalable
• API Gateway and AWS Lambda will help Amazon SageMaker for
throttling, caching, and authorization
• For CI/CD on Amazon SageMaker, we utilize AWS CloudFormation to
provision ready-to-use API Gateway and Lambda

56. Thank you!
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Vit Niennattrakul
vit.n@dailitech.com

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