Scaling_Machine_Learning_at_Holiday_Extras_-_MUC.pdf

by Rebecca Vickery

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Scaling Machine Learning at Holiday Extras REBECCA VICKERY | DATA SCIENTIST @vickdata

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Travel planning is time consuming Airport parking Airport hotels Airport lounges Travel insurance Holiday money Port products Car hire Airport transfers 582 minutes Over 46 days* Travel Planning *Facebook commissioned consumer research company GfK

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Optimising consumer decision making Airport parking Airport hotels Airport lounges Travel insurance Holiday money Port products Car hire Airport transfers Less Hassle. More Holiday Trip recommendations

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The machine learning process Historic Data (Input) (Input Algorithm Learns Mapping Predictions (Output)

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Automated bidding Ad targeting Channel optimisation 1 Ad spend 2 Commercial 3 Customer Experience 4 Marketing Lots of other processes to optimise Automated pricing Allocation Revenue optimisation Automated call handling Personalised experiences Intelligent messaging Optimising send frequency

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Machine learning needs to scale

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Deploying machine learning is hard Scaling is even harder

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Tools - Data Scientists Open source Lack Software Development expertise Mainly Python c Flaticon

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Tools - Software Engineers Different tools Lack ML/Data expertise Mainly Javascript c Flaticon

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Data science process The wrong kind of independence c Flaticon

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People Small data science team Science + software experts are rare c Flaticon

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Two types of deployment

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Bespoke Solutions “Ideas are worth nothing unless executed”, Derek Sivers c Daniel Moyo

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Unused Models Many models never make it to production “Ideas are worth nothing unless executed”, Derek Sivers

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Time to model deployment Model development = days to weeks Model deployment = weeks to never! “Ideas are worth nothing unless executed”, Derek Sivers

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The Google Way

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c Flaticon __init__.py task.py setup.py model.py Model Package

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Repeatable, Reusable Process __init__.py task.py setup.py model.py Model Package

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Not Quite!

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Collaborative Project

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ML Proxy (bespoke ML microservice)

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Monitoring - Model Performance

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Monitoring - AI Platform Performance

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AI Platform A technical solution but also a strategic solution c Google

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The right kind of independence c flaticon Data Scientists can use preferred tools

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The right kind of independence c flaticon Repeatable process for deployment of most models

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Faster time to production c flaticon Fully Managed service

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Faster time to production c flaticon Fast response from Google Cloud Support

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Extensibility c flaticon.com Multiple models and versions

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Customisable c flaticon.com Regularly release support for newer versions of tools

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Time to model deployment Model development = days to weeks Model deployment = hours to days “Ideas are worth nothing unless executed”, Derek Sivers