Getting started with Machine Learning
- 1. Getting Started with Machine Learning Mike Fowler - Senior Site Reliability Engineer - Public Cloud Practice PLACE CUSTOMER LOGO HERE
- 2. • What is Machine Learning? • The AWS Machine Learning Stack • ML Use Cases • Machine Learning: The Forgotten Service • SageMaker Agenda
- 3. London PostgreSQL Meetup January 24th 2019 About Me
- 6. What is … Machine Learning?
- 7. How do Machines Learn? Source: https://towardsdatascience.com/machine-learning-types-2-c1291d4f04b1
- 8. Machine Learning Concepts • Models - Mathematical equation with a solution space approximating the outputs for the given inputs • Feature Engineering - Process of identifying & creating features from the data that will influence/assist the model • Training - Repeated process attempting to find the model that is “just right” such that it does not overfit or underfit the training data
- 9. Dang it Jim, I’m an Engineer not a Mathematician!
- 10. The AWS Machine Learning Stack
- 11. Use Case: Audio Description for Images
- 12. Use Case: Audio Description for Images Lambda
- 13. Use Case: Audio Description for Images Rekognition Image Lambda
- 14. Use Case: Audio Description for Images PollyRekognition Image Lambda
- 15. Use Case: Corporate Updates For All
- 16. Use Case: Corporate Updates For All Transcribe
- 17. Use Case: Corporate Updates For All Transcribe Translate
- 18. Use Case: Corporate Updates For All Transcribe Translate Polly
- 19. Use Case: Corporate Updates For All Transcribe Translate Polly S3
- 20. Use Case: Corporate Updates For All Transcribe Translate Polly S3 CloudFront
- 22. Identify a Problem to Solve Many PagerDuty incidents resolve before I respond disrupting my sleep needlessly
- 23. Identify a Problem to Solve Many PagerDuty incidents resolve before I respond disrupting my sleep needlessly
- 25. Input Data
- 26. Input Data
- 27. Target
- 28. Target
- 29. Target
- 30. Train the Model
- 31. The Lambda Architecture Master Data Serving LayerBatch Layer Speed Layer S3 EMR Kinesis Streams Glue Redshift (Batched Views) DynamoDB (Real-Time Views)
- 32. ML Model The Lambda Architecture + ML Master Data Serving LayerBatch Layer Speed Layer S3 EMR Kinesis Streams Glue Redshift (Batched Views) DynamoDB (Real-Time Views) Amazon Machine Learning
- 33. SageMaker
- 34. Feature Engineering • Most models only take numeric input • Values often need to be constrained - Scale Min/Max - Logarithm • Some values can’t be used - Identifiers - Attributes that wouldn’t be known when making a prediction
- 35. SageMaker
- 36. SageMaker
- 37. Make Predictions
- 38. Mike Fowler mlfowler Questions ? gh-mlfowler mlfowler_ www.mlfowler.com mike.fowler@claranet.uk