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Build a serverless recommendation engine in 72 hours

Build a serverless recommendation engine in 72 hours

Samuel James

October 16, 2019
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  1. Build a serverless recommendation engine in 72 hours
    @samueljabiodun | Samuel James

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  2. @samueljabiodun
    Who Am I?
    − AWS Solutions Architect (Associate)
    − Software Engineer @ Architrave Gmbh
    ✦ PropTech company of the year 2018
    ✦ Over €12 billion in annual transaction volume (including Germany's largest
    single transactions: Sony Centre 2017 & Frankfurt Omni Tower 2018)
    ✦ Over 3,900 managed assets worth €80 billion

    − Technical Author @ HitSubscribe

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  3. @samueljabiodun
    “At some point we’re going to see the first billion-dollar
    startup with a single employee, the founder, and that
    engineer will be using serverless."
    James Governor
    Co-founder at RedMonk

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  4. @samueljabiodun
    In the beginning …

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  5. @samueljabiodun
    Physical Machine
    ❏ Install and configure tons of packages to get your
    application up and run
    ❏ Very Expensive
    ❏ Scaling was an hassle
    ❏ Maintenance was still a problem

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  6. @samueljabiodun

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  7. @samueljabiodun
    Code
    Runtime
    OS
    Hardware
    Physical Servers

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  8. @samueljabiodun
    Code
    Runtime
    OS
    Hardware
    Physical Servers
    Code
    Runtime
    OS
    Hardware
    Virtual Machines
    Code
    Runtime
    OS
    Hardware
    Code
    Runtime
    OS
    Hardware
    Serverless
    Containers

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  9. @samueljabiodun
    Code
    Runtime
    OS
    Hardware
    Serverless
    Serverless

    …computing model that allows you to build
    and run code at scale without thinking about
    servers or infrastructures

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  10. @samueljabiodun
    Serverless and PaaS

    What is the difference?

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  11. @samueljabiodun
    − Scaling
    − Composition
    − Idle State
    Why PaaS Is Not Serverless (FaaS)

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  12. @samueljabiodun
    − Scaling
    − Composition
    − Idle State
    Why PaaS Is Not Serverless (FaaS)

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  13. @samueljabiodun
    Business Logic

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  14. @samueljabiodun
    − Scaling
    − Composition
    − Idle State
    Why PaaS Is Not Serverless (FaaS)

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  15. @samueljabiodun
    Why going serverless?
    − No server provisioning
    − Pay for usage only
    − Infinite scale
    − No server management
    − Quicker time to market
    − Monitoring and logging out of the box

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  16. @samueljabiodun
    "There is no silver bullet. There are
    always options and the options have
    consequences”

    - Ben Horowitz

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  17. @samueljabiodun
    Use cases
    − Data processing (Images,
    Videos etc)
    − Scalable static websites
    − Mobile backends
    − Real-time analysis
    − IoTs
    − Event streaming applications
    − Multi-lingual applications
    − And more…

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  18. @samueljabiodun
    Serverless Recommendation Engine

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  19. @samueljabiodun

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  20. @samueljabiodun

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  21. @samueljabiodun
    Recommendation is about suggesting relevant
    products to your users based on the knowledge
    you have users, products, and users' interactions
    with your products

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  22. @samueljabiodun
    Recommendation is all about data

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  23. @samueljabiodun

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  24. @samueljabiodun

    According to McKinsey & Company, 35%
    of Amazon.com’s revenue is generated by
    its recommendation engine.

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  25. @samueljabiodun
    "Netflix saves up to $1 billion a year via
    its personalised recommendations” 


    – Business Insider

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  26. @samueljabiodun
    Personalised recommendations drive
    sales

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  27. @samueljabiodun
    Data
    Collection
    Data Storage Data Analysis Data Filtering
    Recommendation Phases

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  28. @samueljabiodun
    Data Filtering Techniques
    − Collaborative Filtering
    − Content-Based Filtering
    − Hybrid Filtering

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  29. @samueljabiodun
    Collaborative Filtering
    Interactions of users with products (like movies
    watched, products viewed, products bought etc.

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  30. @samueljabiodun
    Collaborative Filtering Technique in a
    Retail Site

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  31. @samueljabiodun

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  32. @samueljabiodun

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  33. @samueljabiodun
    Content-based Filtering
    Focuses on properties of items. Similarity of items
    is determined by measuring the similarity in their
    properties.

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  34. @samueljabiodun
    Content-based Filtering Technique in a
    Retail Site

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  35. @samueljabiodun
    AWS Rekognition At A Glance

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  36. @samueljabiodun

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  37. @samueljabiodun

    Analysing existing (millions,
    billions of) objects on S3 using
    AWS Batch

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  38. @samueljabiodun

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  39. @samueljabiodun
    Hybrid Filtering
    Combines collaborative filtering and content-
    based filtering

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  40. @samueljabiodun
    Learn about
    your users
    Gain insights in
    to your products

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  41. @samueljabiodun
    Users do search for products
    too, how about adding a visual
    search?

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  42. @samueljabiodun

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  43. @samueljabiodun
    DEMO

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  44. @samueljabiodun

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  45. @samueljabiodun
    Summary
    − Serverless reduces your development time
    − With Serverless, you do not need to be an AI expert to have AI capabilities in your
    app

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  46. @samueljabiodun

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  47. @samueljabiodun
    Thanks!

    Questions?

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