Have you always wanted to add predictive capabilities to your application, but haven’t been able to find the time or the right technology to get started? In this session, learn how an end-to-end smart application can be built in the AWS cloud. We demonstrate how to use Amazon Machine Learning (Amazon ML) to create machine learning models, deploy them to production, and obtain predictions in real-time. We then demonstrate how to build a complete smart application using Amazon ML, Amazon Kinesis, and AWS Lambda. We walk you through the process flow and architecture, demonstrate outcomes, and then dive into the code for implementation. In this session, you learn how to use Amazon ML as well as how to integrate Amazon ML into your applications to take advantage of predictive analysis in the cloud.

1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Alex Ingerman, Product Manager, Amazon ML
October 2015
BDT302
Real-World Smart Applications with
Amazon Machine Learning

2. Agenda
• Why social media + machine learning = happy customers
• Using Amazon ML to find important social media
conversations
• Building an end-to-end application to act on these
conversations

3. Application details
Goal: build a smart application for social media listening in the cloud
Full source code and documentation are on GitHub:
http://bit.ly/AmazonMLCodeSample
Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS
Amazon
Mechanical Turk

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 listening to social media
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. Amazon Machine Learning in one slide
• Easy to use, managed machine learning
service built for developers
• Robust, powerful machine learning
technology based on Amazon’s internal
systems
• Create models using your data already
stored in the AWS cloud
• Deploy models to production in seconds

10. Formulating the problem
We would like to…
Instantly find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether a customer service agent
should act on it, and, if so, send that tweet to customer service
agents.

11. Formulating the problem
We would like to…
Instantly find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether a customer service agent
should act on it, and, if so, send that tweet to customer service
agents.
Twitter API

12. Formulating the problem
We would like to…
Instantly find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether a customer service agent
should act on it, and, if so, send that tweet to customer service
agents.
Twitter API Amazon
Kinesis

13. Formulating the problem
We would like to…
Instantly find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether a customer service agent
should act on it, and, if so, send that tweet to customer service
agents.
Twitter API Amazon
Kinesis
AWS
Lambda

14. Formulating the problem
We would like to…
Instantly find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether a customer service
agent should act on it, and, if so, send that tweet to customer
service agents.
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning

15. Formulating the problem
We would like to…
Instantly find new tweets mentioning @awscloud, ingest and
analyze each one to predict whether a customer service agent
should act on it, and, if so, send that tweet to customer
service agents.
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

16. Building smart applications
Pick the ML
strategy
1
Prepare
dataset
2 3
Create
ML model
4
Write and
configure code
5
Try it out!

17. Picking the machine learning strategy
Question we want to answer:
Is this tweet customer service-actionable, or not?
Our dataset:
Text and metadata from past tweets mentioning @awscloud
Machine learning approach:
Create a binary classification model to answer a yes/no question, and
provide a confidence score

18. Building smart applications
Pick the ML
strategy
1
Prepare
dataset
2 3
Create
ML model
4
Write and
configure code
5
Try it out!

19. Retrieve past tweets
Twitter API can be used to search for tweets containing our
company’s handle (e.g., @awscloud)
import twitter
twitter_api = twitter.Api(**twitter_credentials)
twitter_handle = ‘awscloud’
search_query = '@' + twitter_handle + ' -from:' + twitter_handle
results = twitter_api.GetSearch(term=search_query, count=100, result_type='recent’)
# We can go further back in time by issuing additional search requests

20. Retrieve past tweets
Twitter API can be used to search for tweets containing our
company’s handle (e.g., @awscloud)
import twitter
twitter_api = twitter.Api(**twitter_credentials)
twitter_handle = ‘awscloud’
search_query = '@' + twitter_handle + ' -from:' + twitter_handle
results = twitter_api.GetSearch(term=search_query, count=100, result_type='recent')
# We can go further back in time by issuing additional search requests
Good news: data is well-structured and clean
Bad news: tweets are not categorized (labeled) for us

21. Labeling past tweets
Why label tweets?
(Many) machine learning algorithms work by discovering
patterns connecting data points and labels
How many tweets need to be labeled?
Several thousands to start with
Can I pay someone to do this?
Yes! Amazon Mechanical Turk is a marketplace for tasks that
require human intelligence

22. Creating the Mechanical Turk task

23. Creating the Mechanical Turk task

24. Creating the Mechanical Turk task

25. Creating the Mechanical Turk task

26. Creating the Mechanical Turk task

27. Publishing the task

28. Publishing the task

29. Preview labeling results
Sample tweets from our previously collected dataset + their labels
This column was
created from
Mechanical Turk
responses

30. Preview labeling results
Sample tweets and labels (most metadata fields removed for clarity)

31. Preview labeling results
Sample tweets and labels (most metadata fields removed for clarity)

32. Preview labeling results
Sample tweets and labels (most metadata fields removed for clarity)

33. Preview labeling results
Sample tweets and labels (most metadata fields removed for clarity)

34. Preview labeling results
Sample tweets and labels (most metadata fields removed for clarity)

35. Building smart applications
Pick the ML
strategy
1
Prepare
dataset
2 3
Create
ML model
4
Write and
configure code
5
Try it out!

36. Amazon ML process, in a nutshell
1. Create your datasources
Two API calls to create your training and evaluation data
Sanity-check your data in service console
2. Create your ML model
One API call to build a model, with smart default or custom setting
3. Evaluate your ML model
One API call to compute your model’s quality metric
4. Adjust your ML model
Use console to align performance trade-offs to your business goals

37. Create the data schema string
{
"dataFileContainsHeader": true,
"dataFormat": "CSV",
"targetAttributeName": "trainingLabel",
"attributes": [
{
"attributeName": "description",
"attributeType": "TEXT"
},
<additional attributes here>,
{
"attributeName": "trainingLabel",
"attributeType": "BINARY"
}
]
}
Schemas communicate metadata about your dataset:
• Data format
• Attributes’ names, types, and order
• Names of special attributes

38. Create the training datasource
import boto
ml = boto.connect_machinelearning()
data_spec = {
'DataLocationS3’ : s3_uri # E.g.: s3://my-bucket/dir/data.csv
'DataSchema’ : data_schema } # Schema string (previous slide)
# Use only the first 70% of the datasource for training.
data_spec['DataRearrangement'] = ‘{ "splitting”: {"percentBegin": 0, "percentEnd”: 70 } }’
ml.create_data_source_from_s3( data_source_id = “ds-tweets-train”,
data_source_name = “Tweet training data (70%)”,
data_spec,
compute_statistics = True)

39. Create the evaluation datasource
import boto
ml = boto.connect_machinelearning()
data_spec = {
'DataLocationS3’ : s3_uri # E.g.: s3://my-bucket/dir/data.csv
'DataSchema’ : data_schema } # Schema string (previous slide)
# Use the last 30% of the datasource for evaluation.
data_spec['DataRearrangement'] = ‘{ "splitting”: {"percentBegin": 70, "percentEnd”: 100 } }’
ml.create_data_source_from_s3( data_source_id = “ds-tweets-eval”,
data_source_name = “Tweet evaluation data (30%)”,
data_spec,
compute_statistics = True)

40. Demo: Data exploration

41. Create the ML model
import boto
ml = boto.connect_machinelearning()
ml.create_ml_model( ml_model_id = “ml-tweets”,
ml_model_name = “Tweets screening model”,
ml_model_type = “BINARY”,
training_data_source_id = “ds-tweets-train”)
Input data location is looked up from the training datasource ID
Default model parameters and automatic data transformations are used, or you
can provide your own

42. Evaluate the ML model
import boto
ml = boto.connect_machinelearning()
ml.create_evaluation( evaluation_id = “ev-tweets”,
evaluation_name = “Evaluation of tweet screening model”,
ml_model_id = “ml-tweets”,
evaluation_data_source_id = “ds-tweets-eval”)
Input data location is looked up from the evaluation datasource ID
Amazon ML automatically selects and computes an industry-standard
evaluation metric based on your ML model type

43. Demo: ML model exploration
and adjustment

44. Building smart applications
Pick the ML
strategy
1
Prepare
dataset
2 3
Create
ML model
4
Write and
configure code
5
Try it out!

45. Reminder: Our data flow
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

46. Create an Amazon ML endpoint for retrieving real-
time predictions
import boto
ml = boto.connect_machinelearning()
ml.create_realtime_endpoint(“ml-tweets”)
# Endpoint information can be retrieved using the get_ml_model() method. Sample output:
#"EndpointInfo": {
# "CreatedAt": 1424378682.266,
# "EndpointStatus": "READY",
# "EndpointUrl": ”https://realtime.machinelearning.us-east-1.amazonaws.com",
# "PeakRequestsPerSecond": 200}
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

47. Create an Amazon Kinesis stream for receiving
tweets
import boto
kinesis = boto.connect_kinesis()
kinesis.create_stream(stream_name = ‘tweetStream’, shard_count = 1)
# Each open shard can support up to 5 read transactions per second, up to a
# maximum total of 2 MB of data read per second. Each shard can support up to
# 1000 records written per second, up to a maximum total of 1 MB data written
# per second.
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

48. Set up AWS Lambda to coordinate the data flow
The Lambda function is our application’s backbone. We will:
1. Write the code that will process and route tweets
2. Configure the Lambda execution policy (what is it allowed to do?)
3. Add the Kinesis stream as the data source for the Lambda function
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

49. Create Lambda functions
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS
// These are our function’s signatures and globals only. See GitHub repository for full source.
var ml = new AWS.MachineLearning();
var endpointUrl = '';
var mlModelId = ’ml-tweets';
var snsTopicArn = 'arn:aws:sns:{region}:{awsAccountId}:{snsTopic}';
var snsMessageSubject = 'Respond to tweet';
var snsMessagePrefix = 'ML model '+mlModelId+': Respond to this tweet:
https://twitter.com/0/status/';
var processRecords = function() {…} // Base64 decode the Kinesis payload and parse JSON
var callPredict = function(tweetData) {…} // Call Amazon ML real-time prediction API
var updateSns = function(tweetData) {…} // Publish CS-actionable tweets to SNS topic
var checkRealtimeEndpoint = function(err, data) {…} // Get Amazon ML endpoint URI

50. Create Lambda functions
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS
// These are our function’s signatures and globals only. See GitHub repository for full source.
var ml = new AWS.MachineLearning();
var endpointUrl = '';
var mlModelId = ’ml-tweets';
var snsTopicArn = 'arn:aws:sns:{region}:{awsAccountId}:{snsTopic}';
var snsMessageSubject = 'Respond to tweet';
var snsMessagePrefix = 'ML model '+mlModelId+': Respond to this tweet:
https://twitter.com/0/status/';
var processRecords = function() {…} // Base64 decode the Kinesis payload and parse JSON
var callPredict = function(tweetData) {…} // Call Amazon ML real-time prediction API
var updateSns = function(tweetData) {…} // Publish CS-actionable tweets to SNS topic
var checkRealtimeEndpoint = function(err, data) {…} // Get Amazon ML endpoint URI

51. Configure Lambda execution policy
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS
{ "Statement": [
{ "Action": [ "logs:*” ],
"Effect": "Allow",
"Resource": "arn:aws:logs:{region}:{awsAccountId}:log-group:/aws/lambda/{lambdaFunctionName}:*"
},
{ "Action": [ "sns:publish” ],
"Effect": "Allow",
"Resource": "arn:aws:sns:{region}:{awsAccountId}:{snsTopic}"
},
{ "Action": [ "machinelearning:GetMLModel”, "machinelearning:Predict” ],
"Effect": "Allow",
"Resource": "arn:aws:machinelearning:{region}:{awsAccountId}:mlmodel/{mlModelId}”
},
{ "Action": [ "kinesis:ReadStream”, "kinesis:GetRecords”, "kinesis:GetShardIterator”,
"kinesis:DescribeStream”,"kinesis:ListStreams” ],
"Effect": "Allow",
"Resource": "arn:aws:kinesis:{region}:{awsAccountId}:stream/{kinesisStream}"
}
] }

52. Configure Lambda execution policy
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS
{ "Statement": [
{ "Action": [ "logs:*” ],
"Effect": "Allow",
"Resource": "arn:aws:logs:{region}:{awsAccountId}:log-group:/aws/lambda/{lambdaFunctionName}:*"
},
{ "Action": [ "sns:publish” ],
"Effect": "Allow",
"Resource": "arn:aws:sns:{region}:{awsAccountId}:{snsTopic}"
},
{ "Action": [ "machinelearning:GetMLModel”, "machinelearning:Predict” ],
"Effect": "Allow",
"Resource": "arn:aws:machinelearning:{region}:{awsAccountId}:mlmodel/{mlModelId}”
},
{ "Action": [ "kinesis:ReadStream”, "kinesis:GetRecords”, "kinesis:GetShardIterator”,
"kinesis:DescribeStream”,"kinesis:ListStreams” ],
"Effect": "Allow",
"Resource": "arn:aws:kinesis:{region}:{awsAccountId}:stream/{kinesisStream}"
}
] }
Allow request
logging in
Amazon
CloudWatch

53. Configure Lambda execution policy
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS
{ "Statement": [
{ "Action": [ "logs:*” ],
"Effect": "Allow",
"Resource": "arn:aws:logs:{region}:{awsAccountId}:log-group:/aws/lambda/{lambdaFunctionName}:*"
},
{ "Action": [ "sns:publish” ],
"Effect": "Allow",
"Resource": "arn:aws:sns:{region}:{awsAccountId}:{snsTopic}"
},
{ "Action": [ "machinelearning:GetMLModel”, "machinelearning:Predict” ],
"Effect": "Allow",
"Resource": "arn:aws:machinelearning:{region}:{awsAccountId}:mlmodel/{mlModelId}”
},
{ "Action": [ "kinesis:ReadStream”, "kinesis:GetRecords”, "kinesis:GetShardIterator”,
"kinesis:DescribeStream”,"kinesis:ListStreams” ],
"Effect": "Allow",
"Resource": "arn:aws:kinesis:{region}:{awsAccountId}:stream/{kinesisStream}"
}
] }
Allow
publication of
notifications to
SNS topic

54. Configure Lambda execution policy
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS
{ "Statement": [
{ "Action": [ "logs:*” ],
"Effect": "Allow",
"Resource": "arn:aws:logs:{region}:{awsAccountId}:log-group:/aws/lambda/{lambdaFunctionName}:*"
},
{ "Action": [ "sns:publish” ],
"Effect": "Allow",
"Resource": "arn:aws:sns:{region}:{awsAccountId}:{snsTopic}"
},
{ "Action": [ "machinelearning:GetMLModel”, "machinelearning:Predict” ],
"Effect": "Allow",
"Resource": "arn:aws:machinelearning:{region}:{awsAccountId}:mlmodel/{mlModelId}”
},
{ "Action": [ "kinesis:ReadStream”, "kinesis:GetRecords”, "kinesis:GetShardIterator”,
"kinesis:DescribeStream”,"kinesis:ListStreams” ],
"Effect": "Allow",
"Resource": "arn:aws:kinesis:{region}:{awsAccountId}:stream/{kinesisStream}"
}
] }
Allow calls to
Amazon ML
real-time
prediction APIs

55. Configure Lambda execution policy
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS
{ "Statement": [
{ "Action": [ "logs:*” ],
"Effect": "Allow",
"Resource": "arn:aws:logs:{region}:{awsAccountId}:log-group:/aws/lambda/{lambdaFunctionName}:*"
},
{ "Action": [ "sns:publish” ],
"Effect": "Allow",
"Resource": "arn:aws:sns:{region}:{awsAccountId}:{snsTopic}"
},
{ "Action": [ "machinelearning:GetMLModel”, "machinelearning:Predict” ],
"Effect": "Allow",
"Resource": "arn:aws:machinelearning:{region}:{awsAccountId}:mlmodel/{mlModelId}”
},
{ "Action": [ "kinesis:ReadStream”, "kinesis:GetRecords”, "kinesis:GetShardIterator”,
"kinesis:DescribeStream”,"kinesis:ListStreams” ],
"Effect": "Allow",
"Resource": "arn:aws:kinesis:{region}:{awsAccountId}:stream/{kinesisStream}"
}
] }
Allow reading of
data from
Kinesis stream

56. Connect Kinesis stream and Lambda function
import boto
aws_lambda = boto.connect_awslambda()
aws_lambda.add_event_source(
event_source = 'arn:aws:kinesis:' + region + ':' + aws_account_id + ':stream/' + “tweetStream”,
function_name = “process_tweets”,
role = 'arn:aws:iam::' + aws_account_id + ':role/' + lambda_execution_role)
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

57. Building smart applications
Pick the ML
strategy
1
Prepare
dataset
2 3
Create
ML model
4
Write and
configure code
5
Try it out!

58. Amazon ML real-time predictions
Here is a tweet:

59. Amazon ML real-time predictions
Here is the same tweet…as a JSON blob:
{
"statuses_count": "8617",
"description": "Software Developer",
"friends_count": "96",
"text": "`scala-aws-s3` A Simple Amazon #S3 Wrapper for #Scala 1.10.20 available :
https://t.co/q76PLTovFg",
"verified": "False",
"geo_enabled": "True",
"uid": "3800711",
"favourites_count": "36",
"screen_name": "turutosiya",
"followers_count": "640",
"user.name": "Toshiya TSURU",
"sid": "647222291672100864"
}

60. Amazon ML real-time predictions
Let’s use the AWS Command Line Interface to request a prediction for this tweet:
aws machinelearning predict
--predict-endpoint https://realtime.machinelearning.us-east-1.amazonaws.com
--ml-model-id ml-tweets
--record ‘<json_blob>’

61. DEMOS

62. Recap: Our application’s data flow
Twitter API Amazon
Kinesis
AWS
Lambda
Amazon
Machine Learning
Amazon
SNS

63. Generalizing to more feedback channels
Amazon
Kinesis
AWS
Lambda
Model 1 Amazon
SNS
Model 2
Model 3

64. What’s next?
Try the service:
http://aws.amazon.com/machine-learning/
Download the Social Media Listening application code:
http://bit.ly/AmazonMLCodeSample
Get in touch!
ingerman@amazon.com

65. Thank you!

66. Remember to complete
your evaluations!

67. Demo screenshots

68. Inspecting training data

69. Inspecting and adjusting the ML model