machine learning @ amazon€¦ · • machine translation • visual systems 9/20/16 2. amazon’s...

MachineLearning@AmazonRalfHerbrich

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Overview

• WhatisMachineLearning?• AComputerScienceandStatisticsPerspective• HistoryofMachineLearning

• MachineLearning@Amazon• Forecasting• MachineTranslation• VisualSystems

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Amazon’sVirtuousCycles

Growth CustomerExperience

Traffic

Sellers

Selection & Convenience

LowerPrices

Lower Cost Structure

1. Savingcostsbybetterplanning(e.g.,forecasting)2. Savingcostsbyautomatinghumandecisionmaking(e.g.,pricing)3. Increasingrevenuebylow-frictionexperience(e.g.recommendation)

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Overview



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Overview



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MachineLearning:TheScience

Science• ComputerScience• Statistics• Neuroscience• OperationsResearch

ArtificialIntelligence• Ruleextractionfromdata• Inspiredbyhumanlearning• Adaptivealgorithms

Engineering• Training:DataàModels• Prediction:Modelsà Forecast• Decision:Forecastà Actions

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MachineLearning:AProgramerPerspective

TraditionalProgramming

MachineLearning

ComputerData

ProgramOutput

ComputerData

OutputProgram

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HighatopthestepsofthePyramidofGizaayoungwomanlaughedandcalleddowntohim."Robert,hurryup!IknewIshouldhavemarriedayoungerman!"Hersmilewasmagic.….

MLExamples:Named Entity Extraction

8

Author AnnotatorHighatopthestepsofthePyramidofGizaayoungwomanlaughedandcalleddowntohim."Robert,hurryup!IknewIshouldhavemarriedayoungerman!"Her smilewasmagic.….

if (word is capitalized) and(word before is ‘in’) thenPLACE

else if (word = ‘her’) or (word = ‘his’)or (word = ‘he’) or (word = ‘she’) thenPERSON

...

Data Output (Annotation)

Program

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HighatopthestepsofthePyramidofGizaayoungwomanlaughedandcalleddowntohim."Robert,hurryup!IknewIshouldhavemarriedayoungerman!"Hersmilewasmagic.….

MLExamples:Named Entity Extraction

9

Author Annotator…"Robert,hurryup!IknewIshouldhavemarriedayoungerman!".….

Machine Learning Service

HighatopthestepsofthePyramidofGizaayoungwomanlaughedandcalleddowntohim.…Her smilewasmagic.….

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Overview



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HistoryofMachineLearning

• DeepNeuralNetworks

• Fasthardware(GPUs)

• Distributedcomputingandstorage

• Learning=AdaptationofWeightsinabrain-likelayeredarchitecture

2015("AI")


• Adaptivesystems

• Learning=Scalable,AdaptiveComputationforVariousBigData

2010(“Service”)

•Wideapplicationinproducts

• StatisticalModelingofData

• Learning=ParameterEstimationorInference

2005(“GraphicalModels”)

• StatisticalLearningTheory

• ScoringSystems

• Learning=OptimizationofConvexFunctions

2000(“KernelMachines”)

• ExpertSystems• Decision-TreeLearning(C4.5)

• Learning=MethodstoautomaticallybuildExpertSystems

1990(“Symbolic”)

• NeuralNetworks

• ArtificialIntelligence

• Learning=AdaptationofNeuronsbasedonExternalStimuli

1980(“Neuro”)

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2015("AI")


• Adaptivesystems


2010(“Service”)






• ScoringSystems






• NeuralNetworks



1980(“Neuro”)

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2015("AI")


• Adaptivesystems


2010(“Service”)






• ScoringSystems






• NeuralNetworks



1980(“Neuro”)

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2015("AI")


• Adaptivesystems


2010(“Service”)






• ScoringSystems






• NeuralNetworks



1980(“Neuro”)

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2015("AI")


• Adaptivesystems


2010(“Service”)






• ScoringSystems






• NeuralNetworks



1980(“Neuro”)

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2015("AI")


• Adaptivesystems


2010(“Service”)






• ScoringSystems






• NeuralNetworks



1980(“Neuro”)

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Overview



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MachineLearningOpportunities@Amazon

Retail• DemandForecasting

• VendorLeadTimePrediction

• Pricing• Packaging• SubstitutePrediction

Customers• ProductRecommendation

• ProductSearch• VisualSearch• ProductAds• ShoppingAdvice• CustomerProblemDetection

Seller• FraudDetection• PredictiveHelp• SellerSearch&Crawling

Catalog• Browse-NodeClassification

•Meta-datavalidation

• ReviewAnalysis• HazmatPrediction

Digital• Named-EntityExtraction

• XRay• PlagiarismDetection

• EchoSpeechRecognition

• KnowledgeAcquisiion

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Locations

19

MLSeattle

MLBangalore

S9

A9A2Z

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Ivona

MLBerlin

Evi

Overview

• WhatisMachineLearning?• AComputerScienceandStatisticsPerspective• HistoryofMachineLearning• MachineLearningandArtificialIntelligence


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Forecasting

• Givenpastsalesofaproductineveryregion,predictregionaldemanduptooneyearintothefuture

Setting

• NewProducts:Nopastdemand!• Regionalized:100+fulfillmentcentersworldwide• Sparsity:Hugeskew– manyproductssellveryfewitems• Seasonal:Hugevariationduetoexternal,seasonalevents• Distributions:Futureisuncertainè predictionsmustbedistributions• Scale:20M+productsfulfilledbyAmazonalone!• Orders:Customersdemandbundle ofproducts• Censored:Pastsales≠pastdemand(inventoryconstraint)

Challenges

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DemandForecasting

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Training Range: Non-fashion items have longer training ranges that we can leverage. Need to information share across new and old products.

Seasonality: This item has Christmas seasonality with higher growth over time. This is where we need growth features in addition to date features.

Missing Features or Input: Unexplained spikes in demand are likely caused by missing features or incomplete input data.

Example Softlines product to illustrate the challenges of forecasting.

NewProducts

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Learning across groups of products with varying ages to improve accuracy for new products

New Product Without Sharing: Product is less than 1 year old and hasn’t seen all dates before. Features learned per product are not very strong.

Red = Actual DemandBlack = Forecast

New Product With Sharing: Once we share data across groups of products, we start to see the appropriate lift for new holidays.

Overview



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ASINMachineTranslation

ASINs

Con

tribu

tion

Prof

it

Human Translation

Machine Translation

Selection Gap

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MachineTranslationPipeline

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InputNormalization Tokenization

SentenceSegmentationLowercasing

Translation/Decoding Recasing

Post-processing De-Tokenization

InputRequest

Detection&EscapingofNon-translatables

Re-insertionof(converted)Nontranslatables

TranslatedRequest

MachineTranslation:Deep Dive

p(English |Chinese) = p(English)× p(Chinese | English)p(Chinese)

∝ p(English)× p(Chinese | English)

Language Model

Translation Model

• Language Model: What are fluent English sentences?

• Translation Model: What English sentences account well for a given Chinese sentence?

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Overview



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AutomatedProduceInspection:TheGoal

NewAutomated InspectionCurrent Inspection

Computer Vision

Conclusions

• MachineLearningisanemergingandscientificallyyoungdiscipline!

• MachineLearning“translates”datafromthepastintoaccuratepredictionsaboutthefuture!

• AmazonhasabroadrangeofapplicationsforMachineLearning– it’scentraltoAmazon’sbusiness!

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Thanks!

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machine learning @ amazon€¦ · • machine translation • visual systems 9/20/16 2. amazon’s...

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