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LessonsLearned in ML Modeling and Data Science

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Xavier Amatriain
Xavier Amatriain

Lessons jearned from building real-life Machine Learning systems

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LessonsLearned
LessonsLearned
MoreDatavs.BetterModels
Really? Anand Rajaraman: Former Stanford Prof. & Senior VP at Walmart
Sometimes, it’s not about more data
Norvig: “Google does not have better Algorithms only more Data” Many features/ low-bias models
Sometimes, it’s not about more data
YouMightnotneed allyour“bigData”
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Sometimesyoudoneed aComplexModel
Itpaysofftobesmartabout Hyperparameters
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Supervisedvs.plus UnsupervisedLearning
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Everythingisanensemble
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Theoutputofyourmodel willbetheinputofanotherone (andothersystemdesignproblems)
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Thepains&gains ofFeatureEngineering
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Implicitsignalsbeat explicitones (almostalways)
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bethoughtfulaboutyour TrainingData
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YourModelwilllearn whatyouteachittolearn
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Learntodealwith PresentationBias
More likely to see Less likely
DataandModelsaregreat.Youknowwhat’ sevenbetter? Therightevaluationapproach!
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Youdon’tneedtodistribute yourMLalgorithm
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but,Ifyoudo,youshouldunderstandat whatleveltodoit
The three levels of Distribution/Parallelization ● For each subset of the population (e.g. region) ● For each combination of the hyperparameters ● For each subset of the training data Each level has different requirements ANN Training over distributed GPU’s
somethingsarebetterdone Online and othersoffline…and,thereis Nearlinefor everythinginbetween
System Overview ● Blueprint for multiple personalization algorithm services ● Ranking ● Row selection ● Ratings ● … ● Recommendation involving multi-layered Machine Learning
Matrix Factorization Example
Thetwofacesofyour MLinfrastructure
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Whyyoushouldcareabout answeringquestions(aboutyourmodel)
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Theuntoldstoryof DataScienceandvs.MLengineering
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LessonsLearned in ML Modeling and Data Science
LessonsLearned in ML Modeling and Data Science

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