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

Getting Started with Machine Learning on AWS

Getting Started with Machine Learning on AWS

Machine Learning (ML) is an exciting field that Cloud Computing has helped to accelerate. AWS has played a big part in this with it’s continually expanding range of services from the simply named Machine Learning through to SageMaker. But how do you get started? Thankfully you don’t need to become an expert in linear algebra or statistics, all you need to begin is good idea of the life-cycle of a ML project and a passing familiarity with these AWS services. In this talk we’ll outline a typical ML project and review services such as SageMaker and Rekognition so that you can begin to make use of them in your own projects.

F1e0e0c3c3196a63c9b17a2344fb6a61?s=128

Mike Fowler

October 08, 2019
Tweet

Transcript

  1. @mlfowler_ @Claranet Getting Started with Machine Learning Mike Fowler -

    Senior Site Reliability Engineer - Public Cloud Practice PLACE CUSTOMER LOGO HERE
  2. London PostgreSQL Meetup January 24th 2019 About Me About Me

  3. @mlfowler_ @Claranet

  4. @mlfowler_ @Claranet • What is Machine Learning? • The AWS

    Machine Learning Stack • ML Use Cases • Machine Learning: The Forgotten Service Agenda
  5. @mlfowler_ @Claranet Ethics Source: https://peakcare.wordpress.com/2011/10/05/heads-in-the-sand/

  6. @mlfowler_ @Claranet What is … Machine Learning?

  7. @mlfowler_ @Claranet How do Machines Learn? Source: https://towardsdatascience.com/machine-learning-types-2-c1291d4f04b1

  8. @mlfowler_ @Claranet 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. @mlfowler_ @Claranet Dang it Jim, I’m an Engineer not a

    Mathematician!
  10. @mlfowler_ @Claranet The AWS Machine Learning Stack

  11. @mlfowler_ @Claranet Categories of ML Services

  12. @mlfowler_ @Claranet Categories of ML: Ready to Eat Amazon Comprehend

    Amazon Polly Amazon Rekognition Amazon Textract Amazon Transcribe Amazon Translate
  13. @mlfowler_ @Claranet Use Case: Audio Description for Images

  14. @mlfowler_ @Claranet Use Case: Audio Description for Images Lambda

  15. @mlfowler_ @Claranet Use Case: Audio Description for Images Rekognition Image

    Lambda
  16. @mlfowler_ @Claranet Use Case: Audio Description for Images Polly Rekognition

    Image Lambda
  17. @mlfowler_ @Claranet Use Case: Solving Sudoku Puzzles

  18. @mlfowler_ @Claranet Use Case: Solving Sudoku Puzzles S3

  19. @mlfowler_ @Claranet Use Case: Solving Sudoku Puzzles S3 Lambda

  20. @mlfowler_ @Claranet Use Case: Solving Sudoku Puzzles S3 Rekognition Text

    in Image Lambda
  21. @mlfowler_ @Claranet Use Case: Solving Sudoku Puzzles

  22. @mlfowler_ @Claranet Use Case: Solving Sudoku Puzzles

  23. @mlfowler_ @Claranet Use Case: Solving Sudoku Puzzles

  24. @mlfowler_ @Claranet Use Case: Solving Sudoku Puzzles Have a Play!

    http://bit.ly/comsumsudoku https://github.com/gh-mlfowler/sudokusolve
  25. @mlfowler_ @Claranet Categories of ML: Part Baked Amazon Forecast Amazon

    Personalize
  26. @mlfowler_ @Claranet The Forgotten Service

  27. @mlfowler_ @Claranet Scene: Being On Call https://www.silicon.co.uk/wp-content/uploads/2017/02/Pager.jpg

  28. @mlfowler_ @Claranet Scene: Our Engineer Rests Peacefully Source: https://peakcare.wordpress.com/2011/10/05/heads-in-the-sand/ https://i.pinimg.com/originals/cb/32/5f/cb325f9c268bf2135125f512d95

  29. @mlfowler_ @Claranet Engineer Resting Scene: Red Alert! Source: https://peakcare.wordpress.com/2011/10/05/heads-in-the-sand/ https://vignette.wikia.nocookie.net/memoryalpha/images/6/6b/RedAlert.jpg/revision/latest?cb=20100117050244&path-prefix=en

  30. @mlfowler_ @Claranet Scene: Peace Source: https://peakcare.wordpress.com/2011/10/05/heads-in-the-sand/ https://www.lakelouiseinn.com/wp-content/uploads/2019/01/LakeLouise2-1.jpg

  31. @mlfowler_ @Claranet

  32. @mlfowler_ @Claranet Identify a Problem to Solve Many PagerDuty incidents

    resolve before I respond disrupting my sleep needlessly
  33. @mlfowler_ @Claranet Source Relevant Data

  34. @mlfowler_ @Claranet Input Data

  35. @mlfowler_ @Claranet Input Data

  36. @mlfowler_ @Claranet Target

  37. @mlfowler_ @Claranet Target

  38. @mlfowler_ @Claranet Target

  39. @mlfowler_ @Claranet Train the Model

  40. @mlfowler_ @Claranet The Lambda Architecture Master Data Serving Layer Batch

    Layer Speed Layer S3 EMR Kinesis Streams Glue Redshift (Batched Views) DynamoDB (Real-Time Views)
  41. @mlfowler_ @Claranet 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
  42. @mlfowler_ @Claranet Make Predictions

  43. @mlfowler_ @Claranet Make Predictions

  44. @mlfowler_ @Claranet Make Predictions

  45. @mlfowler_ @Claranet Make Predictions

  46. @mlfowler_ @Claranet Categories of ML: Raw Ingredients Amazon SageMaker Amazon

    Elastic Inference Amazon SageMaker Ground Truth AWS Deep Learning AMIs Apache MXNet on AWS TensorFlow on AWS
  47. @mlfowler_ @Claranet 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
  48. @mlfowler_ @Claranet Fin

  49. @mlfowler_ @Claranet Questions ? Have a Play! http://bit.ly/comsumsudoku https://github.com/gh-mlfowler/sudokusolve

  50. None