Customers are building secure environments in the cloud for data science, analytics, and machine learning. In this session we will introduce the services and features customers are using to build secure data science environments. The session will focus on Amazon SageMaker, a fully managed service supporting the entire machine learning lifecycle, and touch on other supporting AWS services. We are proud to be joined by Royal Bank of Scotland who will walk you through how they are building secure machine learning models on AWS today.

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Secure machine learning
Guarding your data and gaining insights
Jason Barto
Solutions Architect
AWS Financial Services

2. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Put machine learning in the
hands of every developer
Our mission at AWS

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Secure ML requirements
• Protect data against exfiltration
• Prevent unauthorized access
• Protect your business IT systems
• Protect your intellectual property
• Meet regulatory requirements
• Enable agile innovation

4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Key services
Amazon
CloudWatch
AWS
CloudTrail
AWS IAM
AWS KMS
Amazon VPC
Amazon ECR
Amazon S3
Amazon EC2
Amazon
SageMaker

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Amazon SageMaker:
Build, Train, and Deploy ML Models at Scale
1
2
3

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The machine learning lifecycle

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The machine learning lifecycle - Research
“Can we do this?”

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The machine learning lifecycle - Deployment
“How do we scale this?”

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The machine learning lifecycle

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Data lakes on AWS
Snowball
Snowmobile Kinesis
Data Firehose
Kinesis
Data Streams
Amazon S3
Redshift
EMR
Athena Kinesis
Elasticsearch Service
Kinesis
Video Streams
AI Services
QuickSight

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Amazon S3 – Object storage
Secure, highly scalable, durable object storage with millisecond latency for data access
Store any type of data–web sites, mobile apps, corporate applications, and IoT sensors
Security and
Compliance
Three different forms of
encryption; encrypts data
in transit when
replicating across regions;
log and monitor with
CloudTrail, use ML to
discover and protect
sensitive data with Macie
Flexible Management
Classify, report, and
visualize data usage
trends; objects can be
tagged to see storage
consumption, cost, and
security; build lifecycle
policies to automate
tiering, and retention
Durability, Availability
& Scalability
Built for eleven nine’s of
durability; data
distributed across 3
physical facilities in an
AWS region;
automatically replicated
to any other AWS region
Query in Place
Run analytics & ML on
data lake without data
movement; S3 Select can
retrieve subset of data,
improving analytics
performance by 400%

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The machine learning lifecycle

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Secure Jupyter notebooks

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Secure Jupyter notebooks
VPC
Availability zone
Subnet
Notebook
Instance
Availability zone
Subnet
Subnet

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Secure Jupyter notebooks
VPC
Availability zone
Subnet
Notebook
Instance
S3 Endpoint
Availability zone
Subnet
Subnet

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Secure Jupyter notebooks
VPC
Availability zone
Subnet
Notebook
Instance
DatabaseS3 Endpoint
Availability zone
Subnet
Subnet

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Secure Jupyter notebooks
VPC
Availability zone
Subnet
Internet
Notebook
Instance
Database
Web Proxy
S3 Endpoint
Availability zone
Subnet
Subnet

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SageMaker notebook internals
• Customer managed encryption keys
• Lifecycle configurations
• Terminal access
Subnet
Notebook
Instance

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Jupyter terminal

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SageMaker notebook lifecycle

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The machine learning lifecycle

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SageMaker training
VPC
Availability zone
Subnet
Availability zone
Subnet
Subnet
Training
Instance

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SageMaker training
VPC
Availability zone
Subnet
S3 Endpoint
Availability zone
Subnet
Subnet
Training
Instance

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SageMaker training
VPC
Availability zone
Subnet
Internet
Database
Web Proxy
S3 Endpoint
Availability zone
Subnet
Subnet
Training
Instance

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SageMaker training
VPC
Availability zone
Subnet
Internet
Database
Web Proxy
S3 Endpoint
Availability zone
Subnet
Subnet
Training
Instance
Training
Instance

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SageMaker training
VPC
Availability zone
Subnet
Internet
Database
Web Proxy
S3 Endpoint
Availability zone
Subnet
Subnet
Training
Instance
Training
Instance
Training
Instance

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SageMaker training
VPC
Availability zone
Subnet
Internet
Database
Web Proxy
S3 Endpoint
Availability zone
Subnet
Subnet
Training
Instances
Training
Instances
Training
Instances
Encrypted in transit

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SageMaker containers
Container
Training
Instances

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SageMaker training, network isolation
VPC
Availability zone
Subnet
Internet
Database
Web Proxy
S3 Endpoint
Availability zone
Subnet
Subnet
Training
Instances

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Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
TLS

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Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
Encrypted using CMK
TLS

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Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
TLS via private endpoint
TLS

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Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
Stored using CMK
TLS

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Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
TLS
TLS

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Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
Logged using CMK
TLS

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Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
Stored using CMK
TLS

37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
TLS via private endpoint
TLS

38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Lifecycle of training data
VPC
S3 Endpoint Training
Instance
Training start
Training in progress
Training complete
Encrypted using CMK
TLS

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The machine learning lifecycle

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SageMaker hosted endpoints
VPC
Availability zone
Subnet
Internet
Database
Web Proxy
S3 Endpoint
Availability zone
Subnet
Subnet
Inference
Instances

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Endpoint access control

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Endpoint logging and monitoring

43. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

44. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Secure ML requirements
• Protect data against exfiltration
• Prevent unauthorized access
• Protect your business IT systems
• Protect your intellectual property
• Meet regulatory requirements
• Enable agile innovation

45. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Secure ML requirements
• Protect data against exfiltration
• Prevent unauthorized access
• Protect your business IT systems
• Protect your intellectual property
• Meet regulatory requirements
• Enable agile innovation
Identity and access
management
Network controls
VPC Endpoints
Endpoint policies
Activity logs

46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Secure ML requirements
• Protect data against exfiltration
• Prevent unauthorized access
• Protect your business IT systems
• Protect your intellectual property
• Meet regulatory requirements
• Enable agile innovation
Identity and access
management
Network controls
Endpoint policies
Encryption at rest

47. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Secure ML requirements
• Protect data against exfiltration
• Prevent unauthorized access
• Protect your business IT systems
• Protect your intellectual property
• Meet regulatory requirements
• Enable agile innovation
Isolated environments
Isolated networks
IT security policies
Audit logs

48. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Secure ML requirements
• Protect data against exfiltration
• Prevent unauthorized access
• Protect your business IT systems
• Protect your intellectual property
• Meet regulatory requirements
• Enable agile innovation
Identity and access
management
Encryption at rest
VPC Endpoints
Endpoint policies
Audit logs

49. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Secure ML requirements
• Protect data against exfiltration
• Prevent unauthorized access
• Protect your business IT systems
• Protect your intellectual property
• Meet regulatory requirements
• Enable agile innovation
Shared responsibility
model
SOC compliant
ISO compliant
PCI DSS compliant
GDPR compliant

50. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Secure ML requirements
• Protect data against exfiltration
• Prevent unauthorized access
• Protect your business IT systems
• Protect your intellectual property
• Meet regulatory requirements
• Enable agile innovation
Isolated environments
Data lake
On-demand resources
Provided algorithms
Multi-framework

51. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Secure machine learning environments

52. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.