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© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning & Analytics on AWS
Snowball Edge
Kate Werling
Sr Solutions Architect
Amazon Web Services
W P S 3 1 4
Brad Kenstler
Data Scientist II
Amazon Web Services
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Challenge: Run ML Inference at the Edge
with Intermittent Cloud Connectivity!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY
Easily build, train, and deploy machine learning models
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AWS Snowball Edge Compute Optimized with GPU
Power to process more at the edge
• 50 TB local storage
• Amazon Elastic Compute Cloud (Amazon EC2) instances now run on
AWS Snowball Edge Devices
• Sbe-g.12xlarge: 48 vCPU, 192 GB memory
• Ubuntu Server 16.04 LTS
• 10GBase-T, 10/25Gb SFP28, and 40 Gb QSFP+ copper, and optical
networking
• Ruggedized and rack-mountable
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The architecture
1. Train in AWS
2. Deploy to the Edge
3. Operate in the Field
AWS Elemental
Amazon SageMaker Amazon S3
Portland Live Stream
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Kate Werling
Sr Solutions Architect
Amazon Web Services
Brad Kenstler
Data Scientist II
Amazon Web Services
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Machine Learning & Analytics on Snowball Edge (WPS314) - AWS re:Invent 2018

  • 1.
  • 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning & Analytics on AWS Snowball Edge Kate Werling Sr Solutions Architect Amazon Web Services W P S 3 1 4 Brad Kenstler Data Scientist II Amazon Web Services
  • 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The Challenge: Run ML Inference at the Edge with Intermittent Cloud Connectivity!
  • 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high performance algorithms One-click training Hyperparameter optimization BUILD TRAIN DEPLOY Easily build, train, and deploy machine learning models
  • 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS Snowball Edge Compute Optimized with GPU Power to process more at the edge • 50 TB local storage • Amazon Elastic Compute Cloud (Amazon EC2) instances now run on AWS Snowball Edge Devices • Sbe-g.12xlarge: 48 vCPU, 192 GB memory • Ubuntu Server 16.04 LTS • 10GBase-T, 10/25Gb SFP28, and 40 Gb QSFP+ copper, and optical networking • Ruggedized and rack-mountable
  • 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. The architecture 1. Train in AWS 2. Deploy to the Edge 3. Operate in the Field AWS Elemental Amazon SageMaker Amazon S3 Portland Live Stream
  • 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 8. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Kate Werling Sr Solutions Architect Amazon Web Services Brad Kenstler Data Scientist II Amazon Web Services
  • 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.