Questions often arise about training machine learning models using Amazon SageMaker with data from sources other than Amazon S3. In this chalk talk, we dive deep into training models in real time using data from Amazon DynamoDB or a relational database. We demonstrate how training models with Amazon SageMaker is quick and easy, regardless of the data source.

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Train Models on Amazon SageMaker
Using Data Not from Amazon S3
Keith Steward, Ph.D.
ML Specialist Solutions Architect
Amazon Web Services
A I M 4 1 9

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Questions we’ll address
1. What is Amazon SageMaker?
2. What are Amazon DynamoDB and Amazon Relational
Database Service (Amazon RDS)?
3. How to train a machine learning (ML) model with Amazon
SageMaker using data from DynamoDB, Amazon RDS, or
other sources
4. Demo!
5. Q&A

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Amazon SageMaker
• Fully managed service for ML and DL
• Quickly and easily build, train, deploy models at any
scale
• Orchestration platform usable through web console
or programmatically
• Built-in cloud-scale algorithms, DL frameworks, or
bring your own
• Right-size compute as needed, including GPUs
• Notebooks can choose from variety of Amazon EC2 instance types
• Training service uses transient compute to produce model, then
automatically terminates the compute
• Model hosting can use auto-scaling to size dynamically to respond
to inferencing demands from client applications

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Amazon DynamoDB
• Fully managed, multi-region, multi-master
database
• Nonrelational database that delivers reliable
performance at any scale
• Consistent single-digit millisecond latency
• Built-in security, backup and restore, and in-
memory caching
• More than 100,000 AWS customers have chosen
DynamoDB for mobile, web, gaming, ad tech, IoT,
and many other applications that need low-
latency data access

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Amazon RDS
• Makes it easy to set up, operate, and scale a relational
database in the cloud
• Automates time-consuming administration tasks
(hardware provisioning, database setup, patching and
backups, failure detection, repair)
• Fast performance, high availability, security, and
compatibility
• With read replicas, you can scale out beyond the
capacity of a single database for read-heavy database
workloads
• Provides six familiar database engines including Amazon
Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and
Microsoft SQL Server

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Normal feeding of training data to Amazon SageMaker
S3 Data Lake
Amazon
SageMaker
Training svc Hosting svc

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What about feeding training data from other sources?
Amazon
SageMaker
Training svc Hosting svc

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What about feeding training data from other sources?
Amazon
SageMaker
Training svc Hosting svc
S3 Data Lake

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What about feeding training data from other sources?
Amazon
SageMaker
Training svc Hosting svc
S3 Data Lake

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What about feeding training data from other sources?
Amazon
SageMaker
Training svc Hosting svc
S3 Data Lake
AWS Glue

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AWS Glue
• Fully managed serverless extract, transform, and load (ETL)
service that makes it easy to prepare and load data for
analytics and ML
• Create and run an ETL job with a few clicks in the AWS
Management Console
• Works with data stored on AWS in variety of data stores
• Amazon Aurora and all other Amazon RDS engines
• Amazon DynamoDB
• Amazon Redshift
• Relational databases running on Amazon Elastic Compute Cloud (Amazon EC2) in
an Amazon Virtual Private Cloud (Amazon VPC)
• Amazon Simple Storage Service (Amazon S3)
• Discovers your data and stores the associated metadata (e.g.,
table definition and schema) in the AWS Glue Data Catalog
• Generates code that is customizable, reusable, and portable
AWS Glue

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Predict movie ratings from directors
AWS Glue
S3 bucket with
CSV objects
Amazon
SageMaker
Training svc
Hosting svc
Notebook

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19. Thank you!
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The slides and video will be posted to YouTube in the next few days.