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Machine	Learning	@	Amazon
Ralf	Herbrich
11/8/16 1
Background
1992	– 1997	(Berlin,	Diploma)
1997	– 2000	(Berlin,	PhD)
2000	– 2009	(Microsoft	Research)
2009	– 2011	(Microsoft...
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
Machine	Learning:	The	Science
Science
• Computer	Science
• Statistics
• Neuroscience
• Operations	Research
Artificial	Inte...
Machine	Learning:	A	Programer	Perspective
Traditional	Programming
Machine	Learning
Computer
Data
Program
Output
Computer
D...
High	atop	the	steps	of	
the	Pyramid	of	Giza	a	
young	woman	laughed	
and	called	down	to	him.	
"Robert,	
hurry	up!	I	knew	I	...
High	atop	the	steps	of	
the	Pyramid	of	Giza	a	
young	woman	laughed	
and	called	down	to	him.	
"Robert,	
hurry	up!	I	knew	I	...
Amazon’s	Virtuous	Cycles
Growth
Customer
Experience
Traffic
Sellers
Selection &
Convenience
Lower
Prices
Lower Cost
Struct...
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
History	of	Machine	Learning
• Deep	Neural	
Networks
• Fast	hardware	
(GPUs)
• Distributed	
computing	and	
storage
• Learni...
History	of	Machine	Learning
• Deep	Neural	
Networks
• Fast	hardware	
(GPUs)
• Distributed	
computing	and	
storage
• Learni...
History	of	Machine	Learning
• Deep	Neural	
Networks
• Fast	hardware	
(GPUs)
• Distributed	
computing	and	
storage
• Learni...
History	of	Machine	Learning
• Deep	Neural	
Networks
• Fast	hardware	
(GPUs)
• Distributed	
computing	and	
storage
• Learni...
History	of	Machine	Learning
• Deep	Neural	
Networks
• Fast	hardware	
(GPUs)
• Distributed	
computing	and	
storage
• Learni...
History	of	Machine	Learning
• Deep	Neural	
Networks
• Fast	hardware	
(GPUs)
• Distributed	
computing	and	
storage
• Learni...
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
Machine	Learning	Opportunities	@	Amazon
Retail
• Demand	
Forecasting
• Vendor	Lead	Time	
Prediction
• Pricing
• Packaging
...
Locations
20
ML	Seattle
ML	Bangalore
S9
A9
A2Z
11/8/16
Ivona
ML	Berlin
Evi
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
Forecasting
• Given	past	sales	of	a	product	in	every	region,	predict	regional	demand	up	to	one	year	into	the	future
Settin...
Demand	Forecasting
2311/8/16
Training Range: Non-fashion items
have longer training ranges that we
can leverage. Need to i...
New	Products
2411/8/16
Learning across groups of products with varying ages to improve accuracy for new products
New Produ...
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
ASIN	Machine	Translation
ASINs
ContributionProfit
Human Translation
Machine Translation
Selection Gap
11/8/16 26
Machine	Translation	Pipeline
11/8/16 27
Input	Normalization Tokenization
Sentence	SegmentationLowercasing
Translation/Deco...
Machine	Translation:	Deep Dive
p(English |Chinese) =
p(English)× p(Chinese | English)
p(Chinese)
∝ p(English)× p(Chinese |...
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
Scalable	Algorithms	&	Services
• No	limitations	on	model	size	and	data	size!
Setting
• Distributed:	Parameters	need	to	be	...
Three	types	of	data-driven	development
Retrospective
analysis and
reporting
Here-and-now
real-time processing and
dashboar...
Introducing	Amazon	ML
• Easy	to	use,	managed	machine	learning	service	
built	for	developers
• Robust,	powerful	machine	lea...
Overview
• What	is	Machine	Learning?
• A	Computer	Science	and	Statistics	Perspective
• History	of	Machine	Learning
• Machi...
Automated Produce Inspection
New	Automated InspectionCurrent Inspection
Computer Vision
Conclusions
• Machine	Learning	is	an	emerging	and	scientifically	young	discipline!
• Machine	Learning	“translates”	data	fr...
Thanks!
11/8/16 36
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Die Bedeutung von Machine Learning für den e-Commerce am Beispiel von Amazon

Dieser Vortrag gibt Ihnen einen Einblick, warum Maschinelles Lernen von so zentraler Bedeutung für Amazon sowie die Online- und E-Commerce-Industrie ist. Nach einer kurzen Einführung in die Grundlagen von Machine Learning (ML) wird ein Überblick über die heutige Anwendung von ML im Bereich Nachfragevorhersage, Maschinelle Übersetzung und Predictive Analytics Cloud Services gegeben.

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Die Bedeutung von Machine Learning für den e-Commerce am Beispiel von Amazon

  1. 1. Machine Learning @ Amazon Ralf Herbrich 11/8/16 1
  2. 2. Background 1992 – 1997 (Berlin, Diploma) 1997 – 2000 (Berlin, PhD) 2000 – 2009 (Microsoft Research) 2009 – 2011 (Microsoft) 2011 – 2012 (Facebook) 2012 – Present (Amazon)
  3. 3. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 3
  4. 4. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 4
  5. 5. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 5
  6. 6. Machine Learning: The Science Science • Computer Science • Statistics • Neuroscience • Operations Research Artificial Intelligence • Rule extraction from data • Inspired by human learning • Adaptive algorithms Engineering • Training: Data à Models • Prediction: Models à Forecast • Decision: Forecast à Actions 11/8/16 6
  7. 7. Machine Learning: A Programer Perspective Traditional Programming Machine Learning Computer Data Program Output Computer Data Output Program 711/8/16
  8. 8. High atop the steps of the Pyramid of Giza a young woman laughed and called down to him. "Robert, hurry up! I knew I should have married a younger man!" Her smile was magic. …. ML Examples: Named Entity Extraction 8 Author Annotator High atop the steps of the Pyramid of Giza a young woman laughed and called down to him. "Robert, hurry up! I knew I should have married a younger man!" Her smile was magic. …. if (word is capitalized) and (word before is ‘in’) then PLACE else if (word = ‘her’) or (word = ‘his’) or (word = ‘he’) or (word = ‘she’) then PERSON ... Data Output (Annotation) Program 11/8/16
  9. 9. High atop the steps of the Pyramid of Giza a young woman laughed and called down to him. "Robert, hurry up! I knew I should have married a younger man!" Her smile was magic. …. ML Examples: Named Entity Extraction 9 Author Annotator … "Robert, hurry up! I knew I should have married a younger man!". …. Machine Learning Service High atop the steps of the Pyramid of Giza a young woman laughed and called down to him. … Her smile was magic. …. 11/8/16
  10. 10. Amazon’s Virtuous Cycles Growth Customer Experience Traffic Sellers Selection & Convenience Lower Prices Lower Cost Structure 1. Saving costs by better planning (e.g., forecasting) 2. Saving costs by automating human decision making (e.g., pricing) 3. Increasing revenue by low-friction experience (e.g. recommendation) 311/8/16
  11. 11. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 11
  12. 12. History of Machine Learning • Deep Neural Networks • Fast hardware (GPUs) • Distributed computing and storage • Learning = Adaptation of Weights in a brain-like layered architecture 2015 ("AI") • Distributed computing and storage • Adaptive systems • Learning = Scalable, Adaptive Computation for Various Big Data 2010 (“Service”) • Wide application in products • Statistical Modeling of Data • Learning = Parameter Estimation or Inference 2005 (“Graphical Models”) • Statistical Learning Theory • Scoring Systems • Learning = Optimization of Convex Functions 2000 (“Kernel Machines”) • Expert Systems • Decision-Tree Learning (C4.5) • Learning = Methods to automatically build Expert Systems 1990 (“Symbolic”) • Neural Networks • Artificial Intelligence • Learning = Adaptation of Neurons based on External Stimuli 1980 (“Neuro”) 11/8/16 12
  13. 13. History of Machine Learning • Deep Neural Networks • Fast hardware (GPUs) • Distributed computing and storage • Learning = Adaptation of Weights in a brain-like layered architecture 2015 ("AI") • Distributed computing and storage • Adaptive systems • Learning = Scalable, Adaptive Computation for Various Big Data 2010 (“Service”) • Wide application in products • Statistical Modeling of Data • Learning = Parameter Estimation or Inference 2005 (“Graphical Models”) • Statistical Learning Theory • Scoring Systems • Learning = Optimization of Convex Functions 2000 (“Kernel Machines”) • Expert Systems • Decision-Tree Learning (C4.5) • Learning = Methods to automatically build Expert Systems 1990 (“Symbolic”) • Neural Networks • Artificial Intelligence • Learning = Adaptation of Neurons based on External Stimuli 1980 (“Neuro”) 11/8/16 13
  14. 14. History of Machine Learning • Deep Neural Networks • Fast hardware (GPUs) • Distributed computing and storage • Learning = Adaptation of Weights in a brain-like layered architecture 2015 ("AI") • Distributed computing and storage • Adaptive systems • Learning = Scalable, Adaptive Computation for Various Big Data 2010 (“Service”) • Wide application in products • Statistical Modeling of Data • Learning = Parameter Estimation or Inference 2005 (“Graphical Models”) • Statistical Learning Theory • Scoring Systems • Learning = Optimization of Convex Functions 2000 (“Kernel Machines”) • Expert Systems • Decision-Tree Learning (C4.5) • Learning = Methods to automatically build Expert Systems 1990 (“Symbolic”) • Neural Networks • Artificial Intelligence • Learning = Adaptation of Neurons based on External Stimuli 1980 (“Neuro”) 11/8/16 14
  15. 15. History of Machine Learning • Deep Neural Networks • Fast hardware (GPUs) • Distributed computing and storage • Learning = Adaptation of Weights in a brain-like layered architecture 2015 ("AI") • Distributed computing and storage • Adaptive systems • Learning = Scalable, Adaptive Computation for Various Big Data 2010 (“Service”) • Wide application in products • Statistical Modeling of Data • Learning = Parameter Estimation or Inference 2005 (“Graphical Models”) • Statistical Learning Theory • Scoring Systems • Learning = Optimization of Convex Functions 2000 (“Kernel Machines”) • Expert Systems • Decision-Tree Learning (C4.5) • Learning = Methods to automatically build Expert Systems 1990 (“Symbolic”) • Neural Networks • Artificial Intelligence • Learning = Adaptation of Neurons based on External Stimuli 1980 (“Neuro”) 11/8/16 15
  16. 16. History of Machine Learning • Deep Neural Networks • Fast hardware (GPUs) • Distributed computing and storage • Learning = Adaptation of Weights in a brain-like layered architecture 2015 ("AI") • Distributed computing and storage • Adaptive systems • Learning = Scalable, Adaptive Computation for Various Big Data 2010 (“Service”) • Wide application in products • Statistical Modeling of Data • Learning = Parameter Estimation or Inference 2005 (“Graphical Models”) • Statistical Learning Theory • Scoring Systems • Learning = Optimization of Convex Functions 2000 (“Kernel Machines”) • Expert Systems • Decision-Tree Learning (C4.5) • Learning = Methods to automatically build Expert Systems 1990 (“Symbolic”) • Neural Networks • Artificial Intelligence • Learning = Adaptation of Neurons based on External Stimuli 1980 (“Neuro”) 11/8/16 16
  17. 17. History of Machine Learning • Deep Neural Networks • Fast hardware (GPUs) • Distributed computing and storage • Learning = Adaptation of Weights in a brain-like layered architecture 2015 ("AI") • Distributed computing and storage • Adaptive systems • Learning = Scalable, Adaptive Computation for Various Big Data 2010 (“Service”) • Wide application in products • Statistical Modeling of Data • Learning = Parameter Estimation or Inference 2005 (“Graphical Models”) • Statistical Learning Theory • Scoring Systems • Learning = Optimization of Convex Functions 2000 (“Kernel Machines”) • Expert Systems • Decision-Tree Learning (C4.5) • Learning = Methods to automatically build Expert Systems 1990 (“Symbolic”) • Neural Networks • Artificial Intelligence • Learning = Adaptation of Neurons based on External Stimuli 1980 (“Neuro”) 11/8/16 17
  18. 18. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 18
  19. 19. Machine Learning Opportunities @ Amazon Retail • Demand Forecasting • Vendor Lead Time Prediction • Pricing • Packaging • Substitute Prediction Customers • Product Recommendation • Product Search • Visual Search • Product Ads • Shopping Advice • Customer Problem Detection Seller • Fraud Detection • Predictive Help • Seller Search & Crawling Catalog • Browse-Node Classification • Meta-data validation • Review Analysis • Hazmat Prediction Digital • Named-Entity Extraction • XRay • Plagiarism Detection • Echo Speech Recognition • Knowledge Acquisiion 1911/8/16
  20. 20. Locations 20 ML Seattle ML Bangalore S9 A9 A2Z 11/8/16 Ivona ML Berlin Evi
  21. 21. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning and Artificial Intelligence • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 21
  22. 22. Forecasting • Given past sales of a product in every region, predict regional demand up to one year into the future Setting • New Products: No past demand! • Regionalized: 100+ fulfillment centers worldwide • Sparsity: Huge skew – many products sell very few items • Seasonal: Huge variation due to external, seasonal events • Distributions: Future is uncertain è predictions must be distributions • Scale: 20M+ products fulfilled by Amazon alone! • Orders: Customers demand bundle of products • Censored: Past sales ≠ past demand (inventory constraint) Challenges 11/8/16 22
  23. 23. Demand Forecasting 2311/8/16 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.
  24. 24. New Products 2411/8/16 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 Demand Black = Forecast New Product With Sharing: Once we share data across groups of products, we start to see the appropriate lift for new holidays.
  25. 25. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 25
  26. 26. ASIN Machine Translation ASINs ContributionProfit Human Translation Machine Translation Selection Gap 11/8/16 26
  27. 27. Machine Translation Pipeline 11/8/16 27 Input Normalization Tokenization Sentence SegmentationLowercasing Translation/Decoding Recasing Post-processing De-Tokenization Input Request Detection & Escaping of Non-translatables Re-insertion of (converted) Nontranslatables Translated Request
  28. 28. Machine Translation: 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? 11/8/16 28
  29. 29. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 29
  30. 30. Scalable Algorithms & Services • No limitations on model size and data size! Setting • Distributed: Parameters need to be distributed • Fault Tolerance: Data and model chunks might fail • Simplicity: Zero-parameter algorithms for engineers • Any-Time: Any-time convergence of algorithms • Resource-Constrains: Learning algorithms that optimize under resource & budget constraints Challenges 11/8/16 30
  31. 31. Three types of data-driven development Retrospective analysis and reporting Here-and-now real-time processing and dashboards Predictions to enable smart applications Amazon Kinesis Amazon EC2 AWS Lambda Amazon Redshift Amazon RDS Amazon S3 Amazon EMR Amazon Machine Learning
  32. 32. Introducing Amazon ML • Easy to use, managed machine learning service built for developers • Robust, powerful machine learning technology based on Amazon’s internal systems • Create models using your data already stored in the AWS cloud • Deploy models to production in seconds
  33. 33. Overview • What is Machine Learning? • A Computer Science and Statistics Perspective • History of Machine Learning • Machine Learning @ Amazon • Forecasting • Machine Translation • Amazon Machine Learning • Visual Systems 11/8/16 33
  34. 34. Automated Produce Inspection New Automated InspectionCurrent Inspection Computer Vision
  35. 35. Conclusions • Machine Learning is an emerging and scientifically young discipline! • Machine Learning “translates” data from the past into accurate predictions about the future! • Amazon has a broad range of applications for Machine Learning – it’s central to Amazon’s business! 11/8/16 35
  36. 36. Thanks! 11/8/16 36

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