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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Kumar Venkateswar, Product Manager, Amazon AI
6/...
Agenda
Smart applications by example
Developing with Amazon ML
Demo
How Amazon ML fits into other AWS AI services
Q&A
Machine learning and smart applications
Machine learning is the technology that
automatically finds patterns in your data ...
Challenge: Insurance companies spend billions of
dollars on roof losses annually
Solution: BuildFax provides roof age and ...
Challenge: Restaurant owners want to use data to
better serve their customers
Solution: Upserve provides predictions on th...
And a few more examples…
Fraud detection
(binary)
Detecting fraudulent transactions, filtering spam emails,
flagging suspi...
But why not use business rules instead?
Smart applications by counter-example
SELECT c.ID
FROM customers c
LEFT JOIN orders o
ON c.ID = o.customer
LEFT JOIN produ...
Train
model
Evaluate and
optimize
Retrieve
predictions
1 2 3
Building smart applications with Amazon ML
Train
model
Evaluate and
optimize
Retrieve
predictions
1 2 3
Building smart applications with Amazon ML
- Create a Datasou...
Create a Datasource object
>>> import boto3
>>> ml = boto3.client('machinelearning')
>>> ds = ml.create_data_source_from_s...
Explore and understand your data
Train your model
>>> import boto3
>>> ml = boto3.client('machinelearning')
>>> model = ml.create_ml_model(
MLModelId=’my_m...
Train
model
Evaluate and
optimize
Retrieve
predictions
1 2 3
Building smart applications with Amazon ML
- Understand model...
Explore model quality
Fine-tune model interpretation
Fine-tune model interpretation
Train
model
Evaluate and
optimize
Retrieve
predictions
1 2 3
Building smart applications with Amazon ML
- Batch prediction...
Batch predictions
Asynchronous, large-volume prediction generation
Request through service console or API
Best for applica...
Real-time predictions
Synchronous, low-latency, high-throughput prediction generation
Request through service API or serve...
© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Demo
We are here to make machine learning widespread…
by sharing what we use ourselves!
Developer APIs: Rekognition, Polly, Lex...
© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Q&A
Amazon Confidential
What else can we do?
Exploring the Business Use Cases for Amazon Machine Learning - June 2017 AWS Online Tech Talks
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Exploring the Business Use Cases for Amazon Machine Learning - June 2017 AWS Online Tech Talks

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Learning Objectives:
- Learn how to integrate Amazon Machine Learning with applications
- Learn how to train a model using Amazon Machine Learning - Learn how to process semi-structured log data in real-time with Amazon Machine Learning

Machine learning has been used to provide more accurate predictions than hardcoded business logic using available data. For our customers, Amazon Machine Learning is being used from helping restaurant owners, as with Upserve, to determine the right staffing level on a night; to providing more accurate cost estimates in the insurance industry, as with BuildFax. In this tech talk, we'll cover the basics of how to get started with Amazon Machine Learning, and go through an example of how to perform real-time classification of log data using Amazon Machine Learning.

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Exploring the Business Use Cases for Amazon Machine Learning - June 2017 AWS Online Tech Talks

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Kumar Venkateswar, Product Manager, Amazon AI 6/12/2017 Amazon Machine Learning
  2. 2. Agenda Smart applications by example Developing with Amazon ML Demo How Amazon ML fits into other AWS AI services Q&A
  3. 3. Machine learning and smart applications Machine learning is the technology that automatically finds patterns in your data and uses them to make predictions for new data points as they become available Your data + machine learning = smart applications
  4. 4. Challenge: Insurance companies spend billions of dollars on roof losses annually Solution: BuildFax provides roof age and condition estimates with the help of Amazon ML. BuildFax Amazon Machine Learning democratizes the process of building predictive models. It's easy and fast to use, and has machine-learning best practices encapsulated in the product, which lets us deliver results significantly faster than in the past. Joe Emison Founder & Chief Technology Officer
  5. 5. Challenge: Restaurant owners want to use data to better serve their customers Solution: Upserve provides predictions on the number of customers and items ordered with the help of Amazon ML. Upserve Using Amazon Machine Learning, we can predict the total number of customers who will walk through a restaurant’s doors in a night. As a result, restaurateurs can better prep and plan their staffing for that night. Bright Fulton Director of Infrastructure Engineering
  6. 6. And a few more examples… Fraud detection (binary) Detecting fraudulent transactions, filtering spam emails, flagging suspicious reviews, … Personalization (categorical) Recommending content, predictive content loading, improving user experience, … Targeted marketing (binary/categorical) Matching customers and offers, choosing marketing campaigns, cross-selling and up-selling, … Content classification (categorical) Categorizing documents, matching hiring managers and resumes, … Churn prediction (binary) Finding customers who are likely to stop using the service, free-tier upgrade targeting, … Customer support (categorical) Predictive routing of customer emails, social media listening, …
  7. 7. But why not use business rules instead?
  8. 8. Smart applications by counter-example SELECT c.ID FROM customers c LEFT JOIN orders o ON c.ID = o.customer LEFT JOIN products p ON p.ID = o.product GROUP BY c.ID HAVING o.category = ‘toys’ AND ((p.description LIKE ‘%copter%’ AND o.date > GETDATE() - ) OR (COUNT(*) > 2 AND SUM(o.price) > 150 AND o.date > GETDATE() – 40) ) Use machine learning technology to learn your business rules from data, instead of endlessly iterating business rules
  9. 9. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building smart applications with Amazon ML
  10. 10. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building smart applications with Amazon ML - Create a Datasource object pointing to your data - Explore and understand your data - Transform data and train your model
  11. 11. Create a Datasource object >>> import boto3 >>> ml = boto3.client('machinelearning') >>> ds = ml.create_data_source_from_s3( DataSourceId = 'my_datasource', DataSpec= { 'DataLocationS3':'s3://bucket/input/', 'DataSchemaLocationS3':'s3://bucket/input/.schema'}, ComputeStatistics = False)
  12. 12. Explore and understand your data
  13. 13. Train your model >>> import boto3 >>> ml = boto3.client('machinelearning') >>> model = ml.create_ml_model( MLModelId=’my_model', MLModelType='REGRESSION', TrainingDataSourceId='my_datasource')
  14. 14. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building smart applications with Amazon ML - Understand model quality - Adjust model interpretation
  15. 15. Explore model quality
  16. 16. Fine-tune model interpretation
  17. 17. Fine-tune model interpretation
  18. 18. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building smart applications with Amazon ML - Batch predictions - Real-time predictions
  19. 19. Batch predictions Asynchronous, large-volume prediction generation Request through service console or API Best for applications that deal with batches of data records >>> import boto3 >>> ml = boto3.client('machinelearning') >>> model = ml.create_batch_prediction( BatchPredictionId = 'my_batch_prediction’ BatchPredictionDataSourceId = ’my_datasource’ MLModelId = ’my_model', OutputUri = 's3://examplebucket/output/’)
  20. 20. Real-time predictions Synchronous, low-latency, high-throughput prediction generation Request through service API or server or mobile SDKs Best for interaction applications that deal with individual data records >>> import boto3 >>> ml = boto3.client('machinelearning') >>> ml.predict( MLModelId=’my_model', PredictEndpoint=’example_endpoint’, Record={’key1':’value1’, ’key2':’value2’}) { 'Prediction': { 'predictedValue': 13.284348, 'details': { 'Algorithm': 'SGD', 'PredictiveModelType': 'REGRESSION’ } } }
  21. 21. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved Demo
  22. 22. We are here to make machine learning widespread… by sharing what we use ourselves! Developer APIs: Rekognition, Polly, Lex, Amazon Kinesis Analytics Developer platform components: Amazon Machine Learning Data Scientist platform components: Apache Spark MLlib, Apache MXNet, Deep Learning AMI Lower level platform components: Elastic MapReduce, EC2 (CPU, GPU, and FPGA instances) More to come, of course! Amazon Confidential
  23. 23. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved Q&A
  24. 24. Amazon Confidential What else can we do?

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