Are you interested in training and running your machine learning models on Windows? Are you unsure of how to get your data from Microsoft SQL Server to Amazon SageMaker? In this session, we focus on the patterns and practices for machine learning on Windows. This is an interactive question-and-answer session. Please bring your questions, and join us for this discussion. 

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Windows Machine Learning, Deep
Learning, and Artificial Intelligence
Greg Eppel
Sr. Solutions Architect, Microsoft
Amazon Web Services
W I N 3 3 0
Priya Ponnapalli
Data Scientist, AI Labs
Amazon Web Services

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Agenda
Machine learning overview
Windows Deep Learning AMI
Leveraging data in the Microsoft ecosystem
Use cases
Q&A

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Deep
learning
Machine
learning
AI
• AI: Capability of a machine to perform tasks that normally require human intelligence
• ML: Ability to learn without being explicitly programmed
• DL: Techniques for learning in neural networks

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Everyday use of machine learning

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Everyday use of machine learning

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Everyday use of machine learning

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Supervised vs. unsupervised learning
• Dataset with right answers
• Classification – Discrete class label
• Regression – Continuous quantity
• Right answers unknown
• Clustering
• Network analysis

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End-to-end machine learning process
Clean
and
Prepare
Data
Model
Training
Model
Evaluation
Monitor,
Augment
Data
Access
Data
Deploy

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AWS Deep Learning AMI for Windows
• Prebuilt with CUDA 8 and 9
• Prebuilt with several deep learning frameworks
• Apache MXNet
• Caffe
• TensorFlow
• Python 2.7 and Python 3.6 are supported
• P3 instance type is not yet supported
• All other CPU instance types supported

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Microsoft ecosystem with Deep Learning AMI
Amazon
EC2

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Microsoft ecosystem with Amazon SageMaker
Amazon
S3

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A proposed solution
SQL Server
on EC2
Amazon S3
bucket (training)
AWS Database
Migration Service
(AWS DMS)
Amazon
SageMaker
Amazon S3
bucket (test)

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Let’s kick this off with use cases
• Fraud detection in your OLTP data based on usage patterns
• Clickstream analytics to support product pricing or product purchasing
• Customer satisfaction based on sentiment analysis from Dynamics CRM
• Security log analytics – Detecting if usage is normal/abnormal behavior

19. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Greg Eppel
Sr. Solutions Architect, Microsoft
Amazon Web Services
Priya Ponnapalli
Data Scientist, AI Labs
Amazon Web Services

20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.