From the Community Dev Chats at AWS Summit Chicago 2019 Building a Conversational AI Ecosystem on AWS Sage maker Piyali Kamra Enterprise Architect, Director Morningstar

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C HI C AG O

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Building a Conversational AI Ecosystem on AWS Sage maker
Piyali Kamra
Enterprise Architect, Director
Morningstar
D E V 0 8

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Agenda
• Brief Introduction of Case Study / Process Flow
• What/How did we build?
• Serializing a model
• Bundle up a lightweight webserver inside docker
• Local testing of docker image
• Deployment preparation for AWS
• Upload Docker to ECR
• Compress model file (.pkl) using tar.gz and upload to S3.
• Create model configuration, end point configuration and end point on AWS Sage maker.
• Create Lambda function and API Gateway REST API end point (using chalice tool) .
• Test API endpoint using postman and return predictions !
• Integrating with fronting chatbot with API gateway endpoint to get predictions.
• Data collection, preparation, validation.
• Choose algorithm
• Separate data into test/training sets.
• Calculate prediction accuracy percentage.
• Serialize model
Create a .pkl file

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Proactive Intervention + User Engagement Increase + Instant Gratification

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Data cleansing
Run different
algorithms on the
data to get
accurate
classification
Run tests to make
sure
classifications are
accurate
Measure accuracy
of one or model
models
Expose model as
real time API
Test API
Hook up API with
Sales force or JIRA
ticket system so
that we can get
real time
predictions
Based on category (for example sample question
belongs to category “Close My account” a
downstream service could take corrective action like
sending an automated email or alerting a
representative to talk to the customer in order to
prevent them from churning out .
Classification
returned by model
for various sales
force tickets were
as follows:
1)Fees
2)Investment
Advice
3)Close their
Account
4)Sign Up
5)Functionality
Integrate with
natural language
processing bot
Integrate the bot with
different channels
(example websites)

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Classifier Percentage
Naive Bayes 69
SGD Classifier 75
Logistic Regression 85
Word2vec with Logistic Regression 80
Tensorflow /keras 57

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Productionizing the model as an API
• Steps
• Saving the model.
• Create the microservice framework with webserver to act as serve and inference code.
• Build the docker container.
• Test docker container locally.
• Deploy the docker image.
• Upload model artifacts to S3.
• Setting up the model on Sage maker.
• Sage maker endpoint configuration .
• Sage maker endpoint creation.
• Expose the Sage maker Endpoint as an REST API. -> chalice deploy
• Test the API gateway endpoint via Postman.
• Run some test cases for predictions.
**** Chatbot integration with the REST API fronted model on AWS Sagemaker.

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Deploying on AWS Sage maker
• Steps
• Compress the model and upload to S3
• Upload the docker image to ECR
• Create a corresponding model configuration on Sage maker.
• Create an endpoint configuration and endpoint on Sage maker.
• API gateway REST endpoint pointing to Sage maker endpoint.
• Voila ! Get predictions back !!!!

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Thank you!
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Piyali Kamra
Linked In - Piyali Kamra
Email- Piyali.kamra@Morningstar.com

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