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Lab to Factory

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machine learning is our saviour

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https://techcrunch.com/2016/11/26/machine-learning-can-fix-twitter-facebook-and-maybe-even-america/

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machine learning is being democratised

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machine learning

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2006 2011 2016 "machine learning"

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http://www.computerworld.com.au/article/601117/machine-learning-new-face-enterprise-data/

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2006 2011 2016 "machine learning" "data science"

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http://www.kdnuggets.com/2016/01/businesses-need-one-million-data-scientists-2018.html

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https://hbr.org/2016/11/hiring-your-first-chief-ai-officer

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http://www.burtchworks.com/2015/03/02/4-ways-to-spot-a-fake-data-scientist/

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https://www.coursera.org/learn/machine-learning

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http://www.uts.edu.au/future-students/find-a-course/courses/c04293

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2006 2011 2016 "machine learning" "data science"

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2006 2011 2016 "machine learning" "data science" "big data"

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2006 2011 2016 "machine learning" "data science" "big data" "hadoop"

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2006 2011 2016 "hadoop"

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[do palm card version]

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[do palm card version]

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2006 2011 2016 2022

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2006 2011 2016 2022

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as a programmer the probabilities are higher than ever of working on a machine learning project

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What do you do if you get pulled in to a project with a Machine Learning spin on it?

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How do you approach the technology interface between science and engineering?

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How do you approach the people interface between science and engineering?

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What is Machine Learning?

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“The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.” Tom Mitchell Machine Learning

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mispelled Can we accurately identify mispelled words?

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The I Just Read “The Lean Start-Up” Solution 1 def spell_check(word) 2 dictionary = Dictionary.load(file: "dictionary.yaml") 3 if dictionary.has_value?(word) 4 { correct => true } 5 else 6 { correct => false, suggestions => ["Use a dictionary ;)"] } 7 end 8 end

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The I Just Read “TAOCP” Solution 1 int spell_check(Dictionary * dictionary, const char * word, char ** 2 suggestions) { 3 char **ngrams, distanced, suggestions; 4 int err; 5 6 err = generate_within_levenshtein_distance(word, &distanced); 7 if (err != 0) return err; 8 9 err = generate_ngrams(word, &ngrams); 10 if (err != 0) return err; 11 12 err = matching(dictionary, ngrams, distanced, &suggestions); 13 if (err != 0) return err; 14 15 return suggestions; 16 }

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mispelled Can we accurately identify mispelled words?

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Look at how Google does spell checking: it's not based on dictionaries; it's based on word usage statistics of the entire Internet, which is why Google knows how to correct my name, misspelled, and Microsoft Word doesn’t. Joel Spolsky Joel on Software / 2005-10-17

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a valid context. Lots of words that are correctly spelled in a valid context. Lots of words that are correctly spelled in a valid context. Lots of words that are correctly spelled in a valid context. Lots of words that are correctly spelled in a valid context. Lots of words that are correctly spelled in a valid context. Lots of words that are correctly spelled in a valid

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data driven code driven vs

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Fixed Algorithm

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General Purpose

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Can be Simpler

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More Experience

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Some Problems Intractable

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Learning Algorithm

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Restricted Domains

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Improve with Smarter Algorithms

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Improve with More or Better Data

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Can Handle Situations Infeasible for Code Driven Approaches

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Can Be a Really Expensive Way to Encode an If Statement

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Machine Learning Systems

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? ? ? ? ?

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480,189 users 17,770 movies 100,480,507 ratings

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https://www.kaggle.com/c/santander-product-recommendation/data

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2016-06-28,1416856,N,ES,H, 21,2015-07-25,0, 11, 1,,1.0,A,S,N,,KHQ,N,1, 6,"BADAJOZ",1, 38937.48,03 - UNIVERSITARIO 2016-06-28,1202981,N,ES,H, 23,2013-10-18,0, 32, 1,,1.0,I,S,N,,KHE,N,1,29,"MALAGA",0, 56409.06,03 - UNIVERSITARIO 2016-06-28, 137134,N,ES,V, 51,1999-06-30,0, 204, 1,,1.0,A,S,S,,KAT,N,1,28,"MADRID",1, 443237.88,02 - PARTICULARES 2016-06-28,1256662,N,ES,V, 32,2014-05-06,0, 25, 1,,1.0,A,S,N,,KFC,N,1, 2,"ALBACETE",1, 69776.79,03 - UNIVERSITARIO 2016-06-28, 833024,N,ES,V, 36,2009-02-08,0, 88, 1,,1.0,I,S,N,,KFC,N,1,24,"LEON",0, 80136.27,02 - PARTICULARES 2016-06-28, 198396,N,ES,V, 44,2000-10-13,0, 188, 1,,1.0,A,S,N,,KFC,N,1,28,"MADRID",1, 451931.22,02 - PARTICULARES 2016-06-28,1055228,N,ES,H, 43,2012-08-31,0, 45, 1,,1,A,S,N,,KFC,N,1,11,"CADIZ",1, 57271.83,02 - PARTICULARES 2016-06-28,1453594,N,ES,H, 21,2015-09-17,0, 9, 1,,1.0,I,S,N,,KHQ,N,1,15,"CORUÑA, A",0, NA,03 - UNIVERSITARIO 2016-06-28,1114959,N,ES,V, 48,2012-12-28,0, 42, 1,,1,A,S,N,,KFC,N,1, 6,"BADAJOZ",1, 164920.32,02 - PARTICULARES 2016-06-28, 193664,N,ES,H, 90,2000-10-09,0, 189, 1,,1.0,I,S,N,,KAT,N,1,50,"ZARAGOZA",0, 63982.68,02 - PARTICULARES 2016-06-28,1461846,N,ES,H, 22,2015-09-25,0, 9, 1,,1,I,S,N,,KHQ,N,1, 6,"BADAJOZ",0, NA,03 - UNIVERSITARIO 2016-06-28, 281786,N,ES,V, 84,2001-10-13,0, 176, 1,,1,I,S,N,,KAT,N,1,41,"SEVILLA",0, 204135.63,02 - PARTICULARES 2016-06-28, 931057,N,ES,V, 25,2011-08-09,0, 58, 1,,1.0,I,S,N,,KHE,N,1, 8,"BARCELONA",0, 71185.62,03 - UNIVERSITARIO 2016-06-28, 380119,N,ES,H, 66,2002-09-02,0, 166, 1,,1.0,A,S,N,,KFC,N,1,46,"VALENCIA",1, 34973.19,02 - PARTICULARES 2016-06-28, 509236,N,ES,V, 39,2004-12-30,0, 138, 1,,1,A,S,N,,KFC,N,1,41,"SEVILLA",1, 86109.66,02 - PARTICULARES 2016-06-28, 755342,N,ES,V, 51,2008-03-24,0, 99, 1,,1.0,A,S,N,,KAT,N,1, 8,"BARCELONA",1, 29992.74,02 - PARTICULARES 2016-06-28, 678258,N,ES,H, 38,2007-02-20,0, 112, 1,,1.0,I,S,N,,KAT,N,1,28,"MADRID",0, 133180.17,02 - PARTICULARES 2016-06-28, 103307,N,ES,V, 44,1998-08-04,0, 215, 1,,1.0,I,S,S,,KAT,N,1,28,"MADRID",1, 76519.59,02 - PARTICULARES 2016-06-28,1308331,N,ES,H, 22,2014-09-16,0, 21, 1,,1,I,S,N,,KHE,N,1,36,"PONTEVEDRA",0, 134962.29,03 - UNIVERSITARIO 2016-06-28,1006357,N,ES,V, 32,2012-02-27,0, 52, 1,,1,A,S,N,,KFA,N,1,28,"MADRID",1, 65619.90,03 - UNIVERSITARIO 2016-06-28, 124854,N,ES,V, 45,1999-03-10,0, 207, 1,,1,I,S,N,,KAT,N,1, 8,"BARCELONA",0, NA,02 - PARTICULARES 2016-06-28, 757178,N,ES,V, 59,2008-03-31,0, 99, 1,,1.0,A,S,N,,KFC,N,1,28,"MADRID",1, 109184.13,02 - PARTICULARES 2016-06-28, 759426,N,ES,H, 68,2008-04-09,0, 98, 1,,1,I,S,N,,KFA,N,1,28,"MADRID",0, 210710.49,02 - PARTICULARES 2016-06-28,1193227,N,ES,H, 33,2013-10-09,0, 32, 1,,1.0,I,S,N,,KHE,N,1,50,"ZARAGOZA",0, 42343.29,03 - UNIVERSITARIO 2016-06-28,1192797,N,ES,V, 25,2013-10-09,0, 32, 1,,1,I,S,N,,KHE,N,1,33,"ASTURIAS",0, 176043.90,03 - UNIVERSITARIO 2016-06-28,1085653,N,ES,V, 33,2012-10-22,0, 44, 1,,1.0,I,S,N,,KHE,N,1, 8,"BARCELONA",0, 128796.93,03 - UNIVERSITARIO 2016-06-28,1486100,N,ES,H, 22,2015-10-21,0, 8, 1,,1.0,I,S,N,,KHQ,N,1,28,"MADRID",0, NA,03 - UNIVERSITARIO 2016-06-28, 31025,N,ES,V, 61,1996-01-12,0, 245, 1,,1.0,A,S,N,,KAT,N,1,28,"MADRID",1, 140976.18,01 - TOP 2016-06-28,1471619,N,ES,H, 22,2015-10-07,0, 8, 1,,1.0,I,S,N,,KHQ,N,1,19,"GUADALAJARA",0, NA,03 - UNIVERSITARIO

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fecha_dato The table is partitioned for this column ncodpers Customer code ind_empleado Employee index: A active, B ex employed, F filial, N not employee, P pasive pais_residencia Customer's Country residence sexo Customer's sex age Age fecha_alta The date in which the customer became as the first holder of a contract in the bank ind_nuevo New customer Index. 1 if the customer registered in the last 6 months. antiguedad Customer seniority (in months) indrel 1 (First/Primary), 99 (Primary customer during the month but not at the end of the month) ult_fec_cli_1t Last date as primary customer (if he isn't at the end of the month) indrel_1mes Customer type at the beginning of the month ,1 (First/Primary customer), 2 (co-owner ),P (Potential),3 (former primary), 4(former co-owner) tiprel_1mes Customer relation type at the beginning of the month, A (active), I (inactive), P (former customer),R (Potential) indresi Residence index (S (Yes) or N (No) if the residence country is the same than the bank country) indext Foreigner index (S (Yes) or N (No) if the customer's birth country is different than the bank country) conyuemp Spouse index. 1 if the customer is spouse of an employee canal_entrada channel used by the customer to join indfall Deceased index. N/S tipodom Addres type. 1, primary address cod_prov Province code (customer's address) nomprov Province name ind_actividad_cliente Activity index (1, active customer; 0, inactive customer)

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Something showing the reality of where all of these features would have needed to be pulled from

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Something showing what would be required to “execute” on predictions - i.e. plumbing in to decisioning systems, etc

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“We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.” Xavier Amatriain and Justin Basilico Personalisation Science and Engineering at Netflix

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Something showing what the Netflix architecture looks like

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Ambiata - multiple verticals - different data - getting multiple ML systems to production

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Receive data every day Batch score models every day Prepare features every day x N

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- good results - 1. more/better data - 2. better algorithms -> it's a business decision as to which one to focus on -> which has the higher ROI

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Production

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“A wide-spread and uncomfortable trend has emerged: developing and deploying ML systems is relatively fast and cheap, but maintaining them over time is difficult and expensive.” D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, Dan Dennison Hidden Technical Debt in Machine Learning Systems

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Data Acquisition

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data acquisition is non-trivial

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the data will be messy

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format zoo

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most important property of a robust data architecture is have hard edges

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“Traditional abstractions and boundaries may be subtly corrupted or invalidated by the fact that data influences ML system behavior” D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, Dan Dennison Hidden Technical Debt in Machine Learning Systems

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“… Indeed, ML is required in exactly those cases when the desired behavior cannot be effectively expressed in software logic without dependency on external data. The real world does not fit into tidy encapsulation.” D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, Dan Dennison Hidden Technical Debt in Machine Learning Systems

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format zoo data-platform

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format zoo data-platform “the” format

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format zoo data-platform optimise for data-size

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format zoo data-platform optimise for i/o performance

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format zoo data-platform optimise for tooling

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format zoo data-platform security / privacy?

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Data Verification

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“The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.” Tom Mitchell Machine Learning

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the margin of benefit on machine learning systems is often very low

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poor quality data can negate any benefit a good model may give you

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statistics are good at identifying issues in data

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statisticians are good at using statistics to identify issues in data

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the lab is an optimal environment for this

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we can use all the data or statistically equivalent samples

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time is just trying to mess with us

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data will change over time

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data changes must be handled as you go

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data issues must be fixed as you go

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timeliness of data becomes a quality issue

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no escape hatch, you can’t start again

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static checks are important

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absolute thresholds are meh

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proportional thresholds are ok

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statistical properties are good

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anomalies

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anomaly

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need to account for seasonal and growth trends

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breakouts

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breakout

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need to account for seasonal and growth trends

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proportional thresholds are ok

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Feature Engineering

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“At the end of the day, some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used.” Pedro Domingos A Few Useful Things to Know about Machine Learning

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our systems should be linear in data volume not feature count

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we want to be able to throw new features into the mix

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we can’t afford to reprocess historical data

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Model Training

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repeatability

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repeatability

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repeatability

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can we retrain models on demand?

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can we reproduce results independently?

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Model Scoring

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Model Deployment

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Monitoring

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alert fatigue is real

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actionability of alarms needs to be supported by your architecture

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time to verify failures is high

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time to recover failures is high

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cost to recover failures is high

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cost of false negative is high

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cost of false positive is high

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Results Delivery

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Change Management

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we want to be able to do this again

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more models

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better models

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and we don’t want to make a mistake

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Delivery

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delivery anti patterns

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anti-pattern: programmers using open source ML software

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anti-pattern: data scientists scheduling R scripts

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not just programmers

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not just machine learners

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anti-pattern: we can’t say upfront how long it will take to build a good model

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time boxing

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incremental development

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regular reviews

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Lab Factory investigate opportunities system build system operate analyse performance

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anti-pattern: our model performs really well

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know what success is and know how to measure it

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more revenue / profit

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more clicks

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more customers

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less time between actions

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run experiments

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know if impacts are due to you

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anti-pattern: google did it

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latest algorithms aren’t always the answer

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more/better data isn’t always the answer

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an informed ROI discussion is the answer

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“CIOs are in trouble right now… We’ve seen exponential growth in data. If I drop data on the floor and lose it, I am a bad CIO but if my budget grows exponentially to handle it, I am also a bad CIO.” Stephen Probst CTO at Teradata

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Lab to Factory