Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Getting Started with Machine Learning

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

F1e0e0c3c3196a63c9b17a2344fb6a61?s=128

Mike Fowler

April 02, 2019
Tweet

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

  4. None
  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

  39. None