Getting Started with Machine Learning

Have you got data in AWS but don’t know how to get started with Machine Learning? My talk will help you make sense of AWS’ offerings and show you how to use them without having to become a mathematician first. See the full talk on YouTube: https://youtu.be/3phjk1CxhXM

Transcript

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
5. Ethics Source: https://peakcare.wordpress.com/2011/10/05/heads-in-the-sand/
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 Polly Rekognition 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
21. The Forgotten Service
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
24. Source Relevant Data
25. Input Data
26. Input Data
27. Target
28. Target
29. Target
30. Train the Model
31. The Lambda Architecture Master Data Serving Layer Batch 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 Layer Batch 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