AWS Technical Day - Amazon Machine Learning

1. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
Real-World Predictive Applications
with Amazon Machine Learning
Guy Ernest, Solutions Architecture
@guyernest

2. Motivation for ML Modeling
E*
BI
RT
ML

3. https://github.com/awslabs/machine-learning-samples/tree/master/social-media

4. Motivation For Listening To Social Media
Customer is reporting a possible service issue

5. Motivation For Listening To Social Media
Customer is making a feature request

6. Motivation For Listening To Social Media
Customer is angry or unhappy

7. Motivation For Social Media Listening
Customer is asking a question

8. Why Do We Need Machine Learning For This?
The social media stream is high-volume, and most of the
messages are not CS-actionable

9. Why Do We Need Machine Learning For This?
The social media stream is high-volume, and most of the
messages are not CS-actionable

10. Formulating The Problem
We would like to…
Find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether an AWS customer
service agent should take a look at it, and if so, send that
tweet to the customer service queue

11. Formulating The Problem
We would like to…
Find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether an AWS customer
service agent should take a look at it, and if so, send that
tweet to the customer service queue
Twitter API

12. Formulating The Problem
We would like to…
Find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether an AWS customer
service agent should take a look at it, and if so, send that
tweet to the customer service queue
Twitter API Amazon
Kinesis

13. Formulating The Problem
We would like to…
Find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether an AWS customer
service agent should take a look at it, and if so, send that
tweet to the customer service queue
Twitter API Amazon
Kinesis
AWS
Lambda

14. Formulating The Problem
We would like to…
Find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether an AWS customer
service agent should take a look at it, and if so, send
that tweet to the customer service queue
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning

15. Formulating The Problem
We would like to…
Find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether an AWS customer
service agent should take a look at it, and if so, send that
tweet to the customer service queue
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

16. Building Datasource and ML model with the console

17. Splitting to Training/Evalution

18. Console Interlude: Inspect Training Data

19. Console Interlude: Inspect Evaluation & Adjust

20. Console Interlude: Deploy Model To
Production

21. Reminder: Our Intended Data Pipeline
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

22. SNS Subscription to Amazon WorkMail

23. WorkMail Inbox

24. Tweet that needs a response

25. Generalizing To Additional Feedback Channels
Twitter API Amazon
Kinesis
AWS
Lambda
Model 1 Amazon
SNS
Model 2
Model 3