aws january 2016 webinar series - building smart applications with amazon machine learning

© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Alex IngermanSr. Manager, Tech. Product Management, Amazon Machine Learning

1/28/2016

Real-World Smart Applications with Amazon Machine Learning

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

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

AWSLambda

Amazon Machine Learning

AmazonSNS

Amazon Mechanical Turk

http://bit.ly/AmazonMLCodeSample



Motivation for listening to social media

Customer is reporting a possible service issue


Customer is making a feature request


Customer is angry or unhappy


Customer is asking a question

Why do we need machine learning for this?

The social media stream is high-volume, and most of the messages are not CS-actionable

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

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.


We would like to…


Twitter API


We would like to…


Twitter API Amazon Kinesis


We would like to…



AWSLambda


We would like to…



AWSLambda


AmazonSNS

Building smart applications

Pick the ML strategy

1

Preparedataset

2 3

CreateML model

4

Write and configure code

5

Try it out!

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



1

Preparedataset

2 3

CreateML model

4


5

Try it out!

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_handleresults = twitter_api.GetSearch(term=search_query, count=100, result_type='recent’)# We can go further back in time by issuing additional search requests

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_handleresults = 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 cleanBad news: tweets are not categorized (labeled) for us

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

Creating the Mechanical Turk task

Publishing the task

Preview labeling resultsSample tweets from our previously collected dataset + their labels

This column was created from

Mechanical Turk responses

Preview labeling resultsSample tweets and labels (most metadata fields removed for clarity)



1

Preparedataset

2 3

CreateML model

4


5

Try it out!

Amazon ML process, in a nutshell

1. Create your datasourcesTwo API calls to create your training and evaluation dataSanity-check your data in service console

2. Create your ML modelOne API call to build a model, with smart default or custom setting

3. Evaluate your ML modelOne API call to compute your model’s quality metric

4. Adjust your ML modelUse console to align performance trade-offs to your business goals

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

Create the training datasource

import botoml = 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)

Create the evaluation datasource


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)

Visually inspecting training data

Create the ML model


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

Evaluate the ML model


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

Visually inspecting and adjusting the ML model



1

Preparedataset

2 3

CreateML model

4


5

Try it out!

Reminder: Our data flow


AWSLambda


AmazonSNS

Create an Amazon ML endpoint for retrieving real-time predictions


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}


AWSLambda


AmazonSNS

Create an Amazon Kinesis stream for receiving tweets

import botokinesis = 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.


AWSLambda


AmazonSNS

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 tweets2. Configure the Lambda execution policy (what is it allowed to do?)3. Add the Kinesis stream as the data source for the Lambda function


AWSLambda


AmazonSNS

Create Lambda functions


AWSLambda


AmazonSNS

// 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 JSONvar callPredict = function(tweetData) {…} // Call Amazon ML real-time prediction APIvar updateSns = function(tweetData) {…} // Publish CS-actionable tweets to SNS topicvar checkRealtimeEndpoint = function(err, data) {…} // Get Amazon ML endpoint URI

Configure Lambda execution policy


AWSLambda


AmazonSNS

{ "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}"

}] }



AWSLambda


AmazonSNS









}] }

Allow request logging in Amazon

CloudWatch



AWSLambda


AmazonSNS









}] }

Allow publication of notifications to

SNS topic



AWSLambda


AmazonSNS









}] }

Allow calls to Amazon ML

real-time prediction APIs



AWSLambda


AmazonSNS









}] }

Allow reading of data from

Kinesis stream

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)


AWSLambda


AmazonSNS



1

Preparedataset

2 3

CreateML model

4


5

Try it out!

Amazon ML real-time predictions test

Here is a tweet:


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"

}


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>’

https://realtime.machinelearning.us-east-1.amazonaws.com/




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>’{ "Prediction": { "predictedLabel": "0", "predictedScores": { "0": 0.012336540967226028 }, "details": { "PredictiveModelType": "BINARY", "Algorithm": "SGD" } }}




Recap: Our application’s data flow


AWSLambda


AmazonSNS

End-to-end application demo

Generalizing to more feedback channels

Amazon Kinesis

AWSLambda

Model 1 AmazonSNS

Model 2

Model 3

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 [email protected]

http://aws.amazon.com/machine-learning/

http://aws.amazon.com/machine-learning/


mailto:[email protected]

Thank you!

aws january 2016 webinar series - building smart applications with amazon machine learning

Technology