From square to round wheels.... Moving from batch to real-time machine learning

Michael Cutler CTO @Tumra talk at Data Science London 6/09/12

Transcript

1. From square to round wheels...
   Michael Cutler - 6th Sept 2012
   tumra.com
   @tumra
   TUMRA LTD, Building 3, Chiswick Park,
   566 Chiswick High Road, W4 5YA
   ...moving from batch to real-time machine learning
   

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2. Batch
   Processing
   

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3. Credit: http://bit.ly/Q71u4W
   

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4. In Manufacturing...
   Batch processing brought advantages :-
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   Increased scale of production
   ●
   Reduced manufacturing cost
   ●
   Economies of scale (reusable parts)
   However :-
   ●
   Machinery is complex & expensive
   ●
   Each product requires some bespoke parts
   

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5. In Technology...
   Been around since the 50's in Mainframes
   Hadoop (Map/Reduce) advantages :-
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   Increased scale of processing
   ●
   Reduced processing cost **
   ●
   Economies of scale (reusable code)
   However :-
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   Complex & expensive **
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   Most jobs requires some bespoke code
   

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6. Map/Reduce != FUN
   Sure its "just Java" but...
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   Requires certain mindset
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   Multi-stage algorithm complexity
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   If you get stuck, R.T.F.S.
   Alleviated to an extent by tools like :-
   ●
   Pig, Hive, Cascading, Crunch
   Typically requires bespoke code / algorithms
   

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7. Continuous
   Processing
   

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8. Credit: http://bit.ly/NOslqf
   

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9. In manufacturing...
   Described as:
   "a method used to manufacture, produce, or
   process materials without interruption"
   Key features :-
   ●
   Materials are processed in flows & streams
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   Can run continuously (exc. maintenance)
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   Latency e2e can be from seconds to hours
   Credit: Wikipedia
   

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10. In Technology...
    We have a problem... most Hadoop related
    technologies are inherently batch!!
    The trend towards real-time continuous
    computation requires :-
    ●
    New tools (Storm?)
    ●
    Better algorithms
    So what's the solution?
    

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11. Credit: Scott Simmerman
    http://bit.ly/9cxaHt
    

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12. It's a hybrid of both!
    

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13. Batch does have its place...
    Map/Reduce is great for 'boil the ocean' jobs;
    ●
    tasks that take hours or days
    ●
    typically non-interactive with users
    ●
    works well for pattern mining, clustering etc.
    However, the 'perfect' answer is useless if it
    arrives so late it's irrelevant...
    

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14. Real-time machine learning
    Quite simply "data is never at rest"...
    ●
    processed in streams not batches
    ●
    best for 'supervised learning' models
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    end-to-end latency can be in seconds
    Key criteria :-
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    model always has a 'best answer' available
    ●
    feedback used to train the model
    

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16. So what works well in real-time?
    Classification :-
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    Easiest to implement
    Clustering :-
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    Periodically batch recompute clusters
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    Add new data points to the nearest centroid
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    Rinse, repeat
    Collaborative filtering :-
    

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17. The machine learning gap...
    Academic Practical
    

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18. Machine learning gap...
    Academia are 'way out there' with new
    approaches and algorithms almost every day :-
    ●
    Many hard to implement in a parallel way
    We need more focus on :-
    ●
    Inherently distributed algorithms
    ●
    Practical implementations
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    Speed over marginal accuracy improvements
    

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19. Mathematical navel gazing
    We need practical solutions to real-world
    problems...
    Recommendations Rant!?!?!?!?!
    ●
    Most recommenders are 2D matrices
    ●
    Humans are not very 2D
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    Is there an N-dimensional solution?
    

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20. Hybrid approach
    

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21. Hybrid approach
    

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22. Example Use-cases
    Examples;
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    eCommerce optimisation
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    Targeted advertising
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    Financial services (risk modeling)
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    Detecting anomalies in M2M data
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    Automated metadata generation
    ... many more!
    

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23. Almost finished!
    

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24. Introducing TUMRA Labs
    API access to some of our real-time models :-
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    Probabilistic Demographics
    ●
    Language detection **
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    Sentiment analysis **
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    Metadata Generation (entity extraction and
    disambiguation) **
    Free to signup and easy to get started!
    http://labs.tumra.com/
    

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25. Questions?
    tumra.com
    @tumra
    

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