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Amazon Machine Learning: Empowering Developers to Build Smart Applications

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Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning’s powerful algorithms create machine learning (ML) models by finding patterns in your existing data. Then, the service uses these models to process new data and generate predictions for your application. Amazon Machine Learning can ingest data from Amazon S3, Amazon Redshift or Amazon RDS. In this session, we will demonstrate how Amazon Machine Learning can be used to build an ML model, deploy it to production, and query this model from within a smart application.

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Amazon Machine Learning: Empowering Developers to Build Smart Applications

  1. 1. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved Amazon Machine Learning Alex Ingerman – Product Manager, Amazon ML
  2. 2. Agenda • Machine learning and the data ecosystem • Smart applications by example (and counter- example) • Amazon Machine Learning (Amazon ML) features and benefits • Developing with Amazon ML • Q&A
  3. 3. Data is part of the fabric of the applications Front-end and UX Mobile Back-end and operations Data and analytics
  4. 4. Three types of data-driven development Retrospective analysis and reporting Amazon Redshift Amazon RDS Amazon S3 Amazon EMR
  5. 5. Three types of data-driven development Retrospective analysis and reporting Here-and-now real-time processing and dashboards Amazon Kinesis Amazon EC2 AWS Lambda Amazon Redshift, Amazon RDS Amazon S3 Amazon EMR
  6. 6. Three types of data-driven development Retrospective analysis and reporting Here-and-now real-time processing and dashboards Predictions to enable smart applications Amazon Kinesis Amazon EC2 AWS Lambda Amazon Redshift, Amazon RDS Amazon S3 Amazon EMR
  7. 7. 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
  8. 8. 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
  9. 9. Smart applications by example Based on what you know about the user: Will they use your product?
  10. 10. Smart applications by example Based on what you know about the user: Will they use your product? Based on what you know about an order: Is this order fraudulent?
  11. 11. Smart applications by example Based on what you know about the user: Will they use your product? Based on what you know about an order: Is this order fraudulent? Based on what you know about a news article: What other articles are interesting?
  12. 12. And a few more examples… Fraud detection Detecting fraudulent transactions, filtering spam emails, flagging suspicious reviews, … Personalization Recommending content, predictive content loading, improving user experience, … Targeted marketing Matching customers and offers, choosing marketing campaigns, cross-selling and up-selling, … Content classification Categorizing documents, matching hiring managers and resumes, … Churn prediction Finding customers who are likely to stop using the service, free-tier upgrade targeting, … Customer support Predictive routing of customer emails, social media listening, …
  13. 13. Building smart applications – a counter-pattern Dear Alex, This awesome quadcopter is on sale for just $49.99!
  14. 14. Smart applications by counter-example SELECT c.ID FROM customers c LEFT JOIN orders o ON c.ID = o.customer GROUP BY c.ID HAVING o.date > GETDATE() – 30 We can start by sending the offer to all customers who placed an order in the last 30 days
  15. 15. Smart applications by counter-example SELECT c.ID FROM customers c LEFT JOIN orders o ON c.ID = o.customer GROUP BY c.ID HAVING AND o.date > GETDATE() – 30 … let’s narrow it down to just customers who bought toys
  16. 16. Smart applications by counter-example SELECT c.ID FROM customers c LEFT JOIN orders o ON c.ID = o.customer GROUP BY c.ID HAVING o.category = ‘toys’ AND (COUNT(*) > 2 AND SUM(o.price) > 200 AND o.date > GETDATE() – 30) ) … and expand the query to customers who purchased other toy helicopters recently
  17. 17. 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 ‘% %’ AND o.date > GETDATE() - 60) OR (COUNT(*) > 2 AND SUM(o.price) > 200 AND o.date > GETDATE() – 30) ) … but what about quadcopters?
  18. 18. 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) > 200 AND o.date > GETDATE() – 30) ) … maybe we should go back further in time
  19. 19. 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() - 120) OR (COUNT(*) > 2 AND SUM(o.price) > 200 AND o.date > GETDATE() – ) ) … tweak the query more
  20. 20. 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() - 120) OR (COUNT(*) > 2 AND SUM(o.price) > AND o.date > GETDATE() – 40) ) … again
  21. 21. 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) ) … and again
  22. 22. 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!
  23. 23. Why aren’t there more smart applications? 1. Machine learning expertise is rare 2. Building and scaling machine learning technology is hard 3. Closing the gap between models and applications is time-consuming and expensive
  24. 24. Building smart applications today Expertise Technology Operationalization Limited supply of data scientists Many choices, few mainstays Complex and error- prone data workflows Expensive to hire or outsource Difficult to use and scale Custom platforms and APIs Many moving pieces lead to custom solutions every time Reinventing the model lifecycle management wheel
  25. 25. What if there were a better way?
  26. 26. Introducing Amazon ML 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
  27. 27. Easy to use and developer-friendly Use the intuitive, powerful service console to build and explore your initial models – Data retrieval – Model training, quality evaluation, fine-tuning – Deployment and management Automate model lifecycle with fully featured APIs and SDKs – Java, Python, .NET, JavaScript, Ruby, Javascript Easily create smart iOS and Android applications with AWS Mobile SDK
  28. 28. Powerful machine learning technology Based on Amazon’s battle-hardened internal systems Not just the algorithms: – Smart data transformations – Input data and model quality alerts – Built-in industry best practices Grows with your needs – Train on up to 100 GB of data – Generate billions of predictions – Obtain predictions in batches or real-time
  29. 29. Integrated with AWS Data Ecosystem Access data that is stored in S3, Amazon Redshift, or MySQL databases in RDS Output predictions to S3 for easy integration with your data flows Use AWS Identity and Access Management (IAM) for fine-grained data- access permission policies
  30. 30. Fully-managed model and prediction services End-to-end service, with no servers to provision and manage One-click production model deployment Programmatically query model metadata to enable automatic retraining workflows Monitor prediction usage patterns with Amazon CloudWatch metrics
  31. 31. Pay-as-you-go and inexpensive Data analysis, model training, and evaluation: $0.42/instance hour Batch predictions: $0.10/1000 Real-time predictions: $0.10/1000 + hourly capacity reservation charge
  32. 32. Build model Evaluate and optimize Retrieve predictions 1 2 3 Building smart applications with Amazon ML
  33. 33. 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
  34. 34. Create a Datasource object >>> import boto >>> ml = boto.connect_machinelearning() >>> ds = ml.create_data_source_from_s3( data_source_id = ’my_datasource', data_spec= { 'DataLocationS3':'s3://bucket/input/', 'DataSchemaLocationS3':'s3://bucket/input/.schema'}, compute_statistics = True)
  35. 35. Explore and understand your data
  36. 36. Train your model >>> import boto >>> ml = boto.connect_machinelearning() >>> model = ml.create_ml_model( ml_model_id=’my_model', ml_model_type='REGRESSION', training_data_source_id='my_datasource')
  37. 37. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building smart applications with Amazon ML - Understand model quality - Adjust model interpretation
  38. 38. Explore model quality
  39. 39. Fine-tune model interpretation
  40. 40. Fine-tune model interpretation
  41. 41. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building smart applications with Amazon ML - Batch predictions - Real-time predictions
  42. 42. Batch predictions Asynchronous, large-volume prediction generation Request through service console or API Best for applications that deal with batches of data records >>> import boto >>> ml = boto.connect_machinelearning() >>> model = ml.create_batch_prediction( batch_prediction_id = 'my_batch_prediction’ batch_prediction_data_source_id = ’my_datasource’ ml_model_id = ’my_model', output_uri = 's3://examplebucket/output/’)
  43. 43. 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 boto >>> ml = boto.connect_machinelearning() >>> ml.predict( ml_model_id=’my_model', predict_endpoint=’example_endpoint’, record={’key1':’value1’, ’key2':’value2’}) { 'Prediction': { 'predictedValue': 13.284348, 'details': { 'Algorithm': 'SGD', 'PredictiveModelType': 'REGRESSION’ } } }
  44. 44. Architecture patterns for smart applications
  45. 45. Batch predictions with EMR Query for predictions with Amazon ML batch API Process data with EMR Raw data in S3 Aggregated data in S3 Predictions in S3 Your application
  46. 46. Batch predictions with Amazon Redshift Structured data In Amazon Redshift Load predictions into Amazon Redshift -or- Read prediction results directly from S3 Predictions in S3 Query for predictions with Amazon ML batch API Your application
  47. 47. Real-time predictions for interactive applications Your application Query for predictions with Amazon ML real-time API
  48. 48. Adding predictions to an existing data flow Your application Amazon DynamoDB + Trigger event with Lambda + Query for predictions with Amazon ML real-time API
  49. 49. SAN FRANCISCO

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