FOR TAKING TIME TO COME IN AND LEARN 
A BIT MORE. I'M JOHN FRANCIS. I 
PROMISE I'LL TRY TO MAKE THIS AS 
PAINLESS AS POSSIBLE. WE STAND BETWEEN 
YOU AND THE HAPPY HOUR SESSION AFTER 
THIS. WE'LL MAKE SURE WE GOAT THROUGH 
THIS. WE ALSO HAVE A BOOTH IF YOU 
HAVEN'T BEEN TO IT YET. SO IF YOU 
MISS ANYTHING AND WANT TO GO DEEPER 
WITH THE TEAM, FEEL FREE TO SWING 
BY AND CHECK IT OUT. I'LL GIVE A 
LITTLE CONTEXT WHAT WE'RE DOING 
WITH PERSONALIZATION AND WHY IT'S 
IMPORTANT TO STARBUCKS. I'LL QUOTE 
THE OBLIGATORY MACKENZIE SLIDE TO 
SPEAK TO WHY PERSONALIZATION IS 
IMPORTANT. AS YOU CAN SEE HERE FROM 
A COUPLE STATS, 54 OF BUSINESS IS 
SAYING THEY WANT TO DEPLOY MACHINE 
LEARNING CAPABILITIES THE NEXT THREE 
YEARS. SIGNIFICANT INVESTMENT THROUGH 
A LOT OF ENTERPRISES ACROSS FORTUNE 
500 AND THE IN THE SPACE. THE REALITY 
IS I THINK THERE'S SOME TABLE STAKES 
HERE IN TERMS OF PERSONALIZATION. 
ANYONE WHO'S USED AN ECOMMERCE PLATFORM 
LIKE MONS. THIS IS NOT NEW TECHNOLOGY. 
THESE AREN'T NEW CAPABILITIES. YOU 
PROBABLY ARE THINKING, IT FEELS 
LIKE STARBUCKS IS KIND OF LATE TO 
THE GAME HERE. IT'S BEEN A LONG 
TIME. LIKE I SAID IN THE SPACE. 
REALITY IS PERSONALIZATION BEFORE 
WE EVER LOADED A PIECE OF DATA HAS 
REALLY BEEN IN OUR DNA. AS A COMPANY. 
IF YOU THINK ABOUT YOUR OWN EXPERIENCE 
WITH STARBUCKS, AND I'M ASSUMING 
A LOT OF YOU HAVE HAD EXPERIENCES 
AT STARBUCKS. I EVEN SEE CUPS IN 
HERE TODAY. IT'S REALLY ABOUT THE 
ONE TO ONE INTERACTION BETWEEN THE 
CUSTOMER AND THE PARTNER, BARISTAS 
IN THE STORE. I EVEN THINK ABOUT 
ME HOME STORE IN QUEEN ANN. WHEN 
I WALK IN, THE BARISTA KNOWS MY 
NAME, KNOWS MY KIDS' NAMES, WHAT 
THEY LIKE TO ORDER, WHAT I LIKE 
TO ORDER. SO YOU THINK ABOUT IT 
AND, AGAIN, IT'S SORT OF THIS OFFLINE 
EXPERIENCE WITH PERSONALIZATION 
AND HOW THAT REALLY HAS HELPED US 
BUILD AN AMAZING BRAND. SO YOU THINK 
ABOUT THE APPLICATIONS DIGITALLY, 
AND IT REALLY, WHAT WE'RE TRYING 
TO DO AND WHAT MAKES STARBUCKS SPECIAL 
IS IT'S AS MUCH ABOUT THE COFFEE 
AS IT IS THE ELEVATED CUSTOMER EXPERIENCE 
BETWEEN, LIKE I SAID, THE EMPLOYEES 
AT THE STORE AND OUR CUSTOMERS. 
AND REALLY, WE CERTAINLY ARE VERY 
PASSIONATE ABOUT THE COFFEE, BUT 
WE CARE JUST AS MUCH ABOUT THAT 
EXPERIENCE. WE ALSO KNOW OUR CUSTOMERS, 
THROUGH A LOT OF RESEARCH WE'VE 
DONE INTERNALLY, IT'S JUST AS MUCH 
ABOUT THE CONVENIENCE AND CONSISTENCY. 
WHAT WE'VE OBSERVED THE LAST 50 
YEARS ALMOST IS THAT IT'S ACTUALLY 
BECOMING EVEN MORE SO ABOUT THE 
CONVENIENCE AND MORE ABOUT THE CONSISTENCY 
THAT YOU WOULD EXPECT, AND HOW THAT'S 
MANIFESTED ITSELF IS REALLY THROUGH 
THE GROWTH AND ADOLL DEVOPS OF OUR 
MOBILE APPLICATION OF WHICH WE DO 
A TON OF TRANSACQUISITIONS THROUGH 
EVERY DAY AND IT SORT OF, IF YOU 
THINK ABOUT WHERE I STARTED AND 
I TALKED ABOUT HOW WE WANT TO ELEVATE 
THE EXPERIENCE BETWEEN THE BARISTAS 
IN THE STORE AND OUR CUSTOMERS, 
IT FEELS A LITTLE FOREIGN TO THAT, 
RIGHT? WHEN YOU START TALK BEING 
DIGITAL AAPPLICATIONS. WE THINK 
ABOUT ABOUT IT DIFFERENTLY. IN REALLY, 
THE APP ITSELF, AND ANY DIGITAL 
PROPERTIES HAS PROVIDED GOOD SURFACE 
AREA FOR HOW WE CAN TAKE WHAT WE'VE 
DONE AND WHAT'S MADE STARBUCKS SPECIAL 
IN TERMS OF THAT RELAYINGSHIP BETWEEN 
OUR CUSTOMERS AND PARTNERS, AND 
HOW DO WE BRING THAT TO LIFE THROUGH 
THE DIGITAL APPLICATION? A LOT OF 
WAYS, LIKE I SAID, RECOMMENDATION 
SYSTEMS, THEY'VE BEEN COMMODITIZED 
OVER THE YEAR WHAT WE'RE TRYING 
TO DO IS SOMETHING A LITTLE ELEVATED, 
WHICH IS WE REALLY THINK LONG AND 
HARD ABOUT THAT EXPERIENCE THAT 
YOU HAVE IN THE STORE AND HOW DO 
YOU APPLY IT DIGITALLY? SO YOU FEEL 
JUST AS GOOD IN A DIGITAL EXPERIENCE 
AS DO YOU OFFLINE WITH A BARISTA. 
HOW DO WE TRY TO MAKE IT SPECIAL 
AND PERSONAL FOR YOU AS A CUSTOMER? 
IF YOU THINK ABOUT SOME OF THE DATA 
WE HAVE ACCESS TO AND HOW WE CAN 
BUILD THE INGREDIENTS FOR THE PRODUCTS 
YOU'RE BUYING AS WELL AS THE CONTEXT 
IN TERMS OF WHAT TIME OF DAY AND 
WHAT STORE YOU'RE IN GEOGRAPHICALLY, 
AND ALSO AS YOU THINK ABOUT WEATHER. 
YOU CAN TAKE ALL THESE THINGS AND 
HELP REALLY INFORM AN ELEVATED EXPERIENCE 
IN TERMS OF RECOMMENDATIONS WE'RE 
MAKING. I SPEND MOST OF MY TIME 
IN E-MAIL AND POWERPOINT SO I CAN 
GO DEEP INTO THIS PSEUDOARCHITECTURE 
DIAGRAM. REALLY, DEEP AT ITS CORE 
IS A MACHINE LEARNING CAPABILITY 
WE BUILT INTERNALLY AT STARBUCKS 
THAT ALLOWS US, IT SITS ON TOP OF 
AZURE, ALLOWS US TO QUICKLY DEPLOY 
MACHINE LEARNING ALGORITHMS ACROSS 
A NUMBER OF DIFFERENT END POINTS 
AND WHETHER IT'S OUR MOBILE ORDER 
AND PAY EXPERIENCE WITHIN THE APP 
OR IT'S A DRIVE-THROUGH MENU BOARD 
OR I COULD GO ON INTO OTHER AREAS 
WE'RE EXPLORING. IT'S REALLY THINKING 
THROUGH, TO GIVE US EFFECTIVELY 
A PLAYGROUND THAT ALLOWS US TO QUICKLY 
ITERATE AND TEST A LOT OF OTHER 
ALGORITHMS QUICKLY. AB TESTS THEM 
THROUGH THE CUSTOMER EXPERIENCES 
SO WE CAN UNDERSTAND REALLY KIND 
OF IN A CHAMPION CHALLENGE WAY TO 
DEPLOY QUICKLY WITH NOT A LOT OF 
IMPEDIMENTS. THE TEAM WILL GO INTO 
MORE DEEPER INTO THE ARCHITECTURE. 
I WANTED TO SET THE STAGE HERE. 
REALLY, AS WE THINK ABOUT, AGAIN, 
KIND OF DOUBLING DOWN WHY THIS IS 
IMPORTANT TO STARBUCKS, I THINK 
WE'VE HAD SOME INUP ANDEN RECOMMENDATIONS 
AT RECOMMENDATIONS AND JUST TO GIVE 
A SPECIFIC EXAMPLES WHERE WE HAD 
AN EARLY VERSION THAT WE WOULD END 
UP MAKING RECOMMENDATIONS BASED 
ON THINGS LIKE THE WISDOM OF THE 
CROWD, FOR INSTANCE. AND YOU MIGHT, 
AS A VEGETARIAN, YOU MIGHT ACTUALLY 
SEE REPEATEDLY A BACON GOUDA BREAKFAST 
SANDWICH. THE ALGORITHM WASN'T SMART 
ENOUGH TO REALLY UNDERSTAND THAT 
OVER TIME THAT YOU ACTUALLY, IF 
YOU'RE NOT ENGAGE WITH THE RECOMMENDATIONS, 
IT'S PROBABLY SOMETHING WE IMMEDIATE 
TO EVOLVE AND THINK ABOUT DIFFERENTLY. 
SO FAST FORWARD, WE'VE BUILT THE 
INFRASTRUCTURE THAT ALLOWS US TO 
TAKE THE SPIRIT OF COMFORT AND CONNECTION 
BETWEEN, AGAIN, OUR HOW WE THINK 
ABOUT THE RELATIONSHIP BETWEEN OUR 
CUSTOMERS AND BREAST AZURE AND REALLY 
TAKE WHAT THEY INTUITIVELY KNOW 
WHEN THEY INTERACT WITH YOU AND 
HOW DO WE THINK ABOUT IT IN A DIGITAL 
WORLD? FOR INSTANCE, IN THIS EXAMPLE 
WE HAVE A CUSTOMER WHO IS VEGETARIAN, 
LIKE LATTES, YOU CAN SEE THE CHOCOLATE 
CHIP COOKIE DOUGH CAKE POP. WHAT 
WE DO IS OVER TIME, AND WE MAY NOT 
INTUITIVELY KNOW THIS THE SAME WAY 
A A BARISTA WOULD, BUT WE LEARN 
FROM A CUSTOMER'S BEHAVIOR IN TERMS 
OF THE PRODUCTS THEY'RE SUBSTITUTION 
HOW THEY RESPOND TO THE OFFERS WE'RE 
EXTENDING AND THE RECOMMENDATIONS. 
YOU CAN SEE THREE EXAMPLES HERE 
WHERE WE HAVE AN INGREDIENT LEVEL 
OBSERVER THAT LOOKS TO SEE AND IS 
CONSTANTLY MONITORING TO SAY WHAT 
TYPES OF PREFE PREFERENCES AND INGREDIENTS 
DOES A CUSTOMER HAVE? WE CAN EVOLVE 
THE ALGORITHM OVER TIME. OR IF IT'S 
A CATEGORY, A LEVEL OFFICER HOW 
WE CAN OBSERVATION HOW A CUSTOMER 
LIKES BAKED GOODS. OR FRANKLY EVEN 
PRICE AWARENESS. THESE ARE THREE 
EXAMPLES. WE MIGHT SEE A CUSTOMER 
WHO REALLY JUST ENJOYS DEEP BREW 
-- BREWED COFFEE AND MAY NOT NECESSARILY 
WANT AN EXPENSIVE FRAPPUCCINO OR 
HIGHER TICKET ITEM. THEY'RE REALLY 
JUST THREE ILLUSTRATIVE EXAMPLES 
OF WHAT'S GONE INTO THE RECOMMENDATION, 
THE ALGORITHMS ITSELF THAT ALLOW 
US TO MAKE SURE WE'RE CONSTANTLY 
TUNING AND HONING IN ON WHAT WILL 
BE MOST RELEVANT FOR OUR CUSTOMERS. 
JUST TO QUICKLY TOUCH ON THIS, THEN 
HAND OFF TO THE TEAM HERE. HOW WE'RE 
THINKING ABOUT THE PLATFORM WE BUILT. 
THE GOOD NEWS, IT TOOK A LOT OF 
BLOOD, SWEAT, AND TEARS TO GET WHERE 
WE ARE IN TERMS OF CAPABILITY WE 
BUILT IN CLOSE PARTNERSHIP WITH 
OUR TECH FRIENDS ORGANIZATIONALLY. 
NOW THAT WE HAVE THIS INFRASTRUCTURE 
AND CAPABILITY, IT REALLY FRANKLY 
ALLOWS US TO OPEN UP A LOT OF AVENUES 
WHERE WE CAN DEPLOY PRETTY SEAMLESSLY. 
AGAIN, IF YOU REQUEST SAW THE KEYNOTE 
OR COME BAY BY THE CATH, YOU'LL 
SEE DEEP BREW RECOMMENDATIONS ARE 
CURRENTLY RUNNING WITHIN OUR MOBILE 
ORGPAY EXPERIENCE. FEEL FREE TO 
DOWNLOAD THE APP AND TRY IT OUT. 
IF YOU DON'T ALREADY HAVE IT. BUT 
I THINK THAT'S WHERE YOU'LL SEE 
THE MOST PERSONALIZED RECOMMENDATIONS 
BECAUSE WE KNOW A LOT ABOUT YOU 
AS A CUSTOMER IN TERMS OF YOUR BEHAVIOR 
AND YOUR TRANSACTIONS. IT REALLY 
ALLOWS TAOS OPTIMIZE THE MOST. THEN 
GOING TO THE RIGHT, WE'RE CURRENTLY 
PILOTING SOME CAPABILITIES WITHIN 
THE DRIVE-THRU EXPERIENCE. SO IT 
WOULD BE LESS PERSONALIZED. WE'RE 
NOT DOING FACIAL RECOGNITION OR 
ANY LICENSE PLATE SCANNING, ANYTHING 
OF THAT NATURE. REALLY THERE ARE 
ATTRIBUTES WE CAN ASSIGN AT A STORE 
LEVEL AROUND WHAT'S RELEVANT FOR 
THE DEMOGRAPHY OF THAT PARTICULAR 
STORE AND BY LEVERAGING WEATHER 
AS WELL, THEN OF COURSE, VOICE ORDERING 
IS ANOTHER APPLICATION WE HAVE CAPABILITY 
THAT'S CURRENTLY LIVE THAT ALLOWS 
US TO DO TRANSCRIPTION AND LEVERAGE 
THE RECOMMENDATION PLATFORM. WE'RE 
REALLY JUST KIND OF SCRATCHING THE 
SURFACE. I DIDN'T MENTION DIGITAL 
MENU BOARD YET. THAT'S AN AREA, 
AS WE START TO SCALE DEPLOY INTO 
MORE STORES ACROSS OUR 13, 000 +, 
THAT WILL BE CERTAINLY A CANVASS 
FOR US TO THINK HOW TO ELEVATE RECOMMENDATIONS 
WITHIN THAT ENVIRONMENT. THAT WAS 
JUST A LOT OF CONTEXT. I'D LIKE 
TO HAND IT OVER TO THE DIRECTOR 
OF ENGINEERING AND PARTNERED CLOSELY 
WITH US ON THE INFRASTRUCTURE AND 
BACK END. >> THANK YOU, JOHN. TECHNICALLY 
I KNOW WE SAID BETWEEN YOU GUYS 
AND BEERS BUT WE'RE STILL BREWING 
SOMETHING, SO I THINK WE'RE GOOD. 
SO THANKS TO THE CONCEPT THAT JOHN 
OUTLINED. TO SUPPORT THE APPLICATIONS 
WE NEED WORLD CLASS INFRASTRUCTURE. 
AS WE LOOKED AT THE ANALYTICS PLATFORM 
THERE WAS ESSENTIALLY A NEED TO 
HAVE THAT SCALABILITY RELIABILITY 
TO SUPPORT CONSUMER-FACING GLOBAL 
SCALE APPLICATIONS LIKE DEEP BREW. 
WE NEED INFRASTRUCTURE AND ESSENTIALLY 
THE GOALS WERE TO TAKE MODERN ANALYTICS 
CAPABILITIES ACROSS STARBUCKS TO 
ENABLE THE LARGEST AND SMALL ARE 
DATA SCIENCE TEAMS TO DO ANALYTICS 
AT SCALE WITHOUT RESTRICTION. ENTER 
BREW KIT. I WANT TO GET INTO SOME 
OF THE CHALLENGES WE SAW ON THE 
DATA ENGINEERING SIDE BEFORE APPLICATIONS 
LIKE DEEP BREW THAT LED TO FRAMEWORKS 
LIKE BREW KIT BEING DEPLOYED. FROM 
TRADITIONAL TECH STACK SUDDENLY 
WE'RE DEALING WITH SCALED DATA. 
AS WELL AS REALTIME DATA PRODUCTS, 
PLUS A LOT OF UNSTRUCTURED DATA 
. THERE'S AN NONOPTIMAL ENGINEERING 
EXPERIENCE AS WELL. SCALING A LOT 
OF OUR TRAINING, THEY RUN ON CLUSTERS, 
TOOK A LOT OF TIME. ORIOLER INFRASTRUCTURE 
USED TO TAKE 30 TO 40 MINUTES TO 
RUN OUR SPARTAN ENVIRONMENT AND 
DISTRIBUTE PROGRAMMING ENVIRONMENT 
AND HAD TO BUILD OUR OWN FRAMEWORKS 
TO WRITE, MERGE, UPDATE, ON THE 
DATA . NOT TO MENTION PROBLEMS ON 
SYSTEMS AS WELL AS THESE COMPOUNDED 
INTO SLA ISSUES IN PRODUCTION. BECAUSE 
DATA PARTS WERE FRAGMENTED AND THERE 
WAS NO ADHERENCE TO ANY SLAS. ALSO 
NOW, THESE PROBLEMS NOW STARTED 
MANIFESTING THEMSELVES UP TO A DATA 
SCIENCE PERSPECTIVE. THERE WAS A 
NEED FOR COLLOCATED DATA SETS. THE 
EASE OF ACCESS, THE GIRTH OF EXECUTION 
BETWEEN ACTUALLY EXECUTING YOUR 
WORKLOADS AND YOUR JOBS WAS MASSIVE 
BETWEEN THE INFRASTRUCTURE THAT 
COULD ACTUALLY DO IT. LONG LEAD 
TIMES AND DATA FRAGMENTATION AS 
EXPECTED AS WELL AS PLATFORM FRICTION, 
INCREASING COMPLEXITY NOW AS WE 
LOOKALITY SCALING OUT APPLICATIONS, 
DEEP BREW COMES ALONG WITHIN WE 
NOW NEED TO START LOOKING AT DISTRIBUTED 
COMPUTING, DISTRIBUTING GPU PROCESSING, 
ET CETERA. ALSO, THE OLDER INFRASTRUCTURE 
HAD USERS RUNNING MOLDS UNDER THEIR 
DESK IN A BOX. NOW WHEN IT CAME 
TO ENGINEERING, WE HAD TO REWRITE 
THAT TO MAKE AND ENSURE IT'S RUNNING 
AT SCALE AND MOR IMPORTANTLY IT'S 
CLOUD AND SECURITY COMPLIANT. AND 
THE INFRASTRUCTURE LEAD TIME IN 
THIS CASE, REWRITING ALL THESE APPLICATIONS 
WAS MONTHS. ESSENTIALLY THE GOAL 
WE SET OUT TO DO WAS HOW DO WE DEPLOY 
THESE ENVIRONMENTS IN A MATTER OF 
NOT MONTHS, NOT DAYS, BUT IN A MATTER 
OF MINUTES? DO DO WE ENABLE ANALYTICS 
TEAMS ACROSS THE BOARD, ESSENTIALLY 
DEPLOY THESE ENVIRONMENTS AND MAKE 
SURE THEY'RE DEALING WITH THE APPLICATIONS 
AND BUILDING WORLD CLASS STUFF AND 
NOT JUST BATTLING INFRASTRUCTURE. 
BREW KIT. SO BREW KIT ESSENTIALLY 
WAS A FRAMEWORK WE BUILT AS PART 
OF THE OFFERINGS AROUND DEEP BREW 
TO OFFER ZERO FRICTION ANALYTICS. 
ESSENTIALLY WE BUILT A EUROPEFYED 
ANALYTICS PLATFORM TO HANDLE BATCH, 
REALTIME, AND ALL THE ANALYTICS 
WORKLOADS ON A SINGLE TECHNOLOGY 
STACK. WE WANT TO DEBUTS ANY INTERFERENCE 
BETWEEN MODERN DEPLOYMENT AND INFRASTRUCTURE. 
ESSENTIALLY DEALING WITH ALL THE 
PROBLEMMINGS I OUTLINED BEFORE. 
FROM A PLATFORM VALUE PROPOSITION, 
OUR BUSINESS USERS COULD NOW FOCUS 
ON THE APPLICATIONS AND OUTCOME 
NOT FIGURING OUT HOW TO LAND PROVISIONS 
AND SERVICES IN AZURE AND ESSENTIALLY 
ENDING UP WITH A BUNCH OF NONCOMPLIANT 
INFRASTRUCTURE. I'LL TAKE YOU REAL 
QUICK INTO HOW WE ARE ACTUALLY LOOKING 
AT BREW KIT. I KNEW MURPHY'S LAW, 
IF I TRIED TO DEPLOY IT IN MY TEN 
MINUTE, SO I DEPOTTED IT BEFORE 
THIS TALK. ESSENTIALLY I'LL DEBUTS 
ONE AZURE SERVICE IN THIS CASE. 
DATA BRICKS TO. TO ESSENTIALLY SHOW 
THE FEATURES WE'VE DEPLOYED IN THIS 
ENVIRONMENT. TEAMS LIKE JOHN'S ESSENTIALLY 
DEBUTS A BRING YOUR OWN DATA GROUP 
MODEL. ESSENTIALLY WHAT THEY DO 
IS DON'T HAVE TO DEAL WITH LONG 
LEAD TIMES OF INFRASTRUCTURE TEAMS 
DEPLOYING, ADDING USERS TO GROUPS, 
ET CETERA. THEY OWN THEIR OWN GROUPS 
AND ESSENTIALLY REBUILD PROCESSES 
WHICH WE CALL A S IN HOUSE WHICH 
SINKS TO THE DATA ENVIRONMENT SO 
THE USERS THAT NEED TO HAVE ACCESS 
TO THE ENVIRONMENTS ARE SPECIFICALLY 
THE USERS THE BUSINESS WANTS TO 
HAVE ACCESS TO THE ENVIRONMENTS. 
ALL USERS ARE CELEBRATE WITH THE 
RIGHT PERMISSIONS AFTER THE ENVIRONMENT 
IS PROVISIONED. WE ALSO DEBUTS SECURITY 
MOUNTS TO ACCESS ENTERPRISE DATA. 
WHAT IT MEANS IS WE TAKE AWAY THE 
COMPLEXITY OF USERS KNOWING KEYS 
AND PASSWORDS, ET CETERA, NOW TO 
CONFIGURE EACH THEIR OWN ENVIRONMENT. 
THINK OF A LARGE ENTERPRISE LIKE 
STARBUCKS. YOU HAVE EVERY BUSINESS 
GROUP AND HUNDREDS AND HUNDREDS 
OF USERS, IT BECOMES A MESS PRETTY 
QUICKLY. ENTERPRISE MOUNTS. WHAT 
WE NEEDED TO DO WAS ESSENTIALLY 
-- I'LL TRY TO WRITE THIS TO OUR 
ENTERPRISE MOUNT HERE. ESSENTIALLY 
WHAT WE NEEDED TO DO WAS MAKE SURE 
THAT ENTERPRISE DATA WHICH IS INDUSTRIALIZED 
DATA SETS WE ARE PROVISIONING ARE 
NOT WRITABLE. WE DIDN'T WANT USERS 
ESSENTIALLY BE ABLE TO WRITE -- 
IT SHOULD BE READ ONLY ACCESS TO 
ENTERPRISE DATA PRODUCTS. IN THIS 
CASE, FOR EXAMPLE, I COULDN'T WRITE 
TO IT. WE HAD DATA SCIENTISTS DEPLOYING. 
WE HAD DINE DATA SCIENTISTS WRITING 
PYTHON, A HUGE SWATH OF USERS WHO 
ESSENTIALLY WERE ONLY USING SQL 
TO READ THEIR DATA. ESSENTIALLY 
WE BUILT OUR OWN META SYNC BACKED 
ON A HIGH META STORE TO SYNC THE 
PUBLISHED DATA ACROSS DIFFERENT 
USER ENVIRONMENTS. IT GAVE US THE 
FLEXIBILITY TO ESSENTIALLY ISOLATE 
ENVIRONMENTS BASED ON DEBUTSAGE 
AND BUSINESS USE CASE. THE STORAGE. 
I TOLD YOU YOU COULDN'T WRITE TO 
PUBLISHED LAYER AND ENTERPRISE DATA 
ASSETS. USERS AND APPLICATIONS LIKE 
DEEP BREW NEEDED THE FLEXIBILITY 
TO WRITE PERSONAL STORAGE AS WELL 
AS RIGHT TO SPECIFIC TEAMWORK SPACES 
WHICH IS WHERE WE PROVIDED APPLICATION 
STORAGE. THEN WE ALSO NOW DEPLOYED 
ALL THE SCALING, BEST IN THE -- 
MOST WELL CONFIGURED AND OPTIMIZED 
CUTTERS TO SUPPORT DIFFERENT WORKLOADS, 
FOR EXAMPLE, IF DEEP BREW NEEDED 
SPECIFIC GPU CONFIGURATIONS TO RUN 
TRAINING ON A DAILY BASIS, WE PROVIDED 
THAT FUNCTIONALITY OUT OF THE BOX. 
ANOTHER OF MY FAVORITES, ESSENTIALLY 
TO DEMOCRATIZE ANALYTICS ACROSS 
THE ORGANIZES WE STARTED DEPLOYING 
STARTER NOTEBOOKS TO TRULY DEMOCRATIZE 
THIS ACROSS DATA SCIENTISTS AND 
ANALYTICS USERS REGARDLESS OF SKILL 
LEVEL. WE ALSO USING PILOTING A 
LOT OF OUR MACHINE LEARNING USE 
CASES USING AZURE ML. AZURE ML IS 
HELPING US DEPLOY A FULLY FUNCTIONAL 
MODEL LIFE CYCLE END TO END. WE 
ALSO DEBUTS DEVOPS TEMPLATES TO 
DEPLOY AND MONITOR PIPELINES. AZURE 
DATA FACTORY FOR ORCHESTRATION. 
LAST BUT NOT LEAST, THIS KIND OF 
DECENTRALIZED MODEL HELPS US START 
TRACKING THE COST OF DEPLOYMENTS 
IN A VERY FINE GRAIN MANNER. ESSENTIALLY 
APPLICATIONS LIKE DEEP BREW NOW 
HAVE EXTREME HIGH INSIGHTS WHAT 
THAT YOU SPENDING ON THE SERVICES 
COSTING THEM AS WELL AS WHAT TO 
DO TO OPTIMIZE THAT APPLICATION. 
SWITCHING BACK OVER. ESSENTIALLY 
LIKE I SAID, THE DATA SCIENCE TEMPLATE 
FOR AN APPLICATION LIKE DEEP BREW 
USES PLATEAU SERVICES. FOR THE REST 
END POINT, APP SERVICES, A COMBINATION 
OF COSMOS DB AS WELL AS RELATIONAL 
DATABASE RAYER AND KEY WORK FOR 
KEY MANAGEMENT. YOU CAN READ THE 
REST OF THE DIFFERENT SERVICES. 
I WANT TO HIGHLIGHT ALSO, THIS IS 
POWERED BY EXCELLENCE AND ENGINEERING 
FROM A STARBUCKS AUTO NATION PERSPECTIVE 
WHERE WE DO ESSENTIALLY ONE-CLICK 
DEPOTMENT OF OUR TEMPLATE ACROSS 
AZURE SERVICES. THE DEPLOYMENT TIME 
ESSENTIALLY IS LESS THAN 15 MINUTES, 
WHICH MEANS WE CAN DEPLOY THIS TO 
ANY ANALYTICS TEAM OR BUSINESS GROUP 
THAT NEED AZURE FULLY FUNCTIONING 
ANALYTICS TO CREATE WHORL CLASS 
APPLICATIONS LIKE DEEP BREW IN A 
MATTER OF MINUTES. WE PROVIDE AUDIT 
FRAMEWORKS AS WELL AS GETTING STARTED 
AS WELL AS THE META SYNC WHICH IS 
TOOLING WE BUILT ON TOP OF THE STACK. 
ESSENTIALLY, NOW WE HAVE HUNDREDS 
OF USERS ONBOARD ON THE STACK AND 
I'M NOW -- ONCE WE PROVISION BREW 
KIT WE ESSENTIALLY WERE ABLE TO 
DEPLOY APPLICATIONS LIKE DEEP BREW. 
I'LL NOW HAVE DATA SCIENTISTS COME 
UP TO TALK ABOUT THE NUTS AND BOLTS 
OF DEEP BREW. ED. >> THANKS. EVERYBODY. 
I'M A DATA SCIENTIST AT STARBUCKS. 
JUST BY SHOW OF HANDS, LIKE TO SEE 
IF ANY OTHER DATA SCIENTISTS OR 
PRACTICING MACHINE LEARN ROG OUT 
THERE. RAISE YOUR HAND. OKAY. MAYBE 
10. THAT'S GREAT. IF YOU ARE A DATA 
SCIENTIST OR WORKING WITH MACHINE 
LEARNING, YOU KNOW IT CAN BE A CHALLENGE 
TO DEPLOY YOUR MODELS TO PRODUCTION 
TO REAL WORLD CUSTOMER-FACING APPLICATIONS. 
AND YOU NEED TO MAKE SURE YOU HAVE 
THE SYSTEMS IN PRAYS FOR SECURITY, 
RELIABILITY, SCALABILITY, AND THINGS 
LIKE PRIVACY. IT ALL HAS TO BE ADDRESSED. 
IT'S GREAT HAVING A PARTNER IN MICROSOFT 
AND STARBUCKS TECH TO HAVE US DO 
THAT IN AZURE. OUR FIRST USE CASE 
WAS COMMANDER. FOCUSING ON A COUPLE 
OF BUSINESS OBJECTIVES. WE WANTED 
TO PROVIDE THE RECO RECOMMENDER 
IN STARBUCKS ORDER AND MOBILE PAY 
APPLICATION. THE TWO MAIN OBJECTIVES 
WERE TO MAXIMIZE THE PROBABILITY 
OF A CUSTOMER PURCHASING AN ADDITIONAL 
ITEM. TWO ITEMS ALREADY PURCHASED 
IN THEIR CART AND/OR INCREASE OR 
MAXIMIZE THE REVENUE OR TOTAL TICKET 
FOR THE SALE. SO THE IDEA FROM MARKETING 
PARLANCE IS UPSELL OR CROSS-SELL 
PRODUCTS. WE BUILT A RECOMMENDER 
IN OUR DEEP BREW PLATFORM, NAMED 
IT PURPLE FISH. SO THE MAIN THING 
WAS A COUPLE OF MAIN KEY POINTS, 
WE WANTED TO LEARN ABOUT CUSTOMER 
PRODUCT PREFERENCES FAST CONTINUOUSLY. 
SO TO DO THAT, WE NEEDED AN ALGORITHM 
ADAPT. AND OBSERVE THE INDIVIDUAL 
CUSTOMER PURCHASES THROUGH THEIR 
HISTORY AS WELL AS INTERACTION WITH 
OUR RECOMMENDER. WE NEEDED TO BE 
PROACTIVE ON A COUPLE OF CASES LIKE 
IF THE USER HASN'T YET ADDED A PRODUCT 
TO THEIR CART OR IF THE USER HAS 
NO HISTORY. THAT'S AN ISSUE. IT'S 
KNOWN AS THE COLD START PROBLEM 
SO YOU HAVE NO HISTORY ABOUT THE 
USER OR POSSIBLY ABOUT A NEW PRODUCT. 
AS YOU KNOW, STARBUCKS HAS SEASONAL 
PRODUCTS HAVE LIMITED TIME OFFER 
PRODUCTS. WE NEED TO ADDRESS THAT 
AS WELL. AND A KEY FEATURE IS BEING 
EXTENSIBILITY. WITH RECOMMENDATION 
SYSTEMS THERE'S A LOT OF CHOICES. 
COLLABORATIVE FILTERING, BANDITS, 
THINGS LIKE FP GROWTH. WE WANTED 
TO BE ABLE TO HAVE A FLEXIBLE SYSTEM 
TO DEPLOY MANY TYPES OF ALGORITHMS 
AND TEST THEM AGAINST EACH OTHER. 
I'LL TALK A LITTLE ABOUT SOME OF 
THE INTUITION AND SCIENCE BEHIND 
RECOMMENDER. SO HOW DO WE LEARN 
ABOUT THE WORLD? THERE'S THIS IDEA 
THAT FOR PEOPLE WE TRY TO OBSERVE 
THE WORLD. WE HAVE CERTAIN DID YOU 
BELIEFS. THEY'RE ALTERED BASED ON 
OUR OFFICER OR EVIDENCE ABOUT THE 
WORLD. SO ON THE LEFT, YOU HAVE 
A FORM OF BASE LAW. SO THIS WAS 
A KEY ELEMENT TO THE FIRST RECOMMENDER. 
AND YOU HAVE AN EQUATION HERE WHICH 
BASICALLY ALLOWS YOU TO UPDATE PROBABILITY 
DISTRIBUTION. AND THAT'S YOUR POSTERIOR. 
SO YOU TAKE A PRIOR ABOUT YOUR BELIEF 
WHICH MAY BE A FORM PRIOR, SO YOU 
DON'T REALLY KNOW MUCH. AS YOU HAVE 
MULTIPLY THAT BY THE LIKELIHOOD, 
WHICH IS OUTLINED OUR DATA GENERATION 
PROCESS, BIGNOMIAL. A PERSON MAY 
OR MAY NOT BE VEGETARIAN. HEADS 
OR TAILS. YOU MIGHT REMEMBER THAT 
FROM STATISTICS OR PROBABILITY CLASS 
IF YOU TOOK IT. WE DIVIDE IT OAF 
A NORMALIZATION VALUE AND WE GET 
A POSTERIOR DISTRIBUTION. AND THE 
BOTTOM RIGHT YOU SEE THE BETA DISTRIBUTION 
AND INITIALLY THERE'S A VERY UNINFORMED 
PRIOR AND AS WE GET MORE INFORMATION 
FROM THE EVIDENCE ABOUT A CUSTOMER 
PURCHASE BEHAVIOR, WE CAN INCREASE 
OUR CONFIDENCE, WHICH IS SHOWN BY 
THE NARROWING OF THAT DISTRIBUTION 
AND THAT POINT, AT THE PEAK, WE 
HAVE A BELIEF WE HAVE HIGH CONFIDENCE 
IN IT. OKAY. A LITTLE MORE ABOUT 
HOW IT WORKS. WE HAD TO BREAK UP 
THE PROBLEM INTO TWO PHASES. FIRST, 
IT'S A PRETTY BIG STATE SPACE. SO 
A LOT OF DIFFERENT PRODUCTS, COMBINATIONS, 
AND PAIRINGS. WE REALLY WANTED TO 
NARROW DOWN THE OPTIONS. CONTEXT 
IS VERY IMPORTANT TO PERSONALIZE 
THOSE THINGS WILL INFLUENCE YOUR 
CHOICES AND DECISIONS ABOUT WHAT 
PRODUCTS YOU'LL PURCHASE AS WELL 
AS USER HISTORY. WE NARY HE DOWN 
THE OPTIONS FIRST BY UNDERSTANDING 
THE STATE. WE -- I'LL GO INTO IT 
IN A MINUTE IN MORE DEPTH ABOUT 
THE CONTEXT OBSERVERS. THEY'RE USED 
TO DETERMINE SOME HIDDEN OR IMPLICIT 
PREFERENCES. SO AS MENTIONED, CAN 
WE DETERMINE IF YOU'RE VEGETARIAN 
OR IF YOU HAVE A PREFERENCE FOR 
A PARTICULAR TYPE OF PRODUCT, CATEGORY, 
LIKE BLENDED WHICH IS LIKE A LOT 
OF FRAPPUCCINOS. THEN WE CAN OPTIMIZE 
BASED ON PRICE SENSITIVITY. THEN 
THERE'S THIS NECESSITY TO USER ENFORCEMENT 
LEARNING. WE NEED TO CONSTANTLY 
ADAPT AND CONTINUE TO LEARN. WE 
DETERMINE WHAT WE HAVE SHOWN A USER 
AND HOW THEY'VE RESPONDED ZERO. 
KIND OF A CONVERSION IN A WEB PAGE. 
GET THE FEEDBACK IN A THE SYSTEM. 
THEN GENERATE A LIST OF PRODUCT 
RECOMMENDATIONS BASED ON A LOT OF 
THOSE THINGS I MENTIONED, LIKE THE 
STATE. THE PRODUCT PAIRING FOR BOTH 
USERS OR IF THE USER DOESN'T HAVE 
ANY HISTORY, THE WISDOM OF THE CROWD 
IF YOU HAVE NO HISTORY ON THE USER. 
THEN ON TOP OF THAT, YOU CAN TAKE 
THAT RECOMMENDATION, I'LL SHOW IN 
A MINUTE, THEN ADD THE CONTEXT OBSERVERS. 
WE'LL LOOK HOW IT WORKS IN A SECOND. 
LASTLY, IF YOU HAVE MULTIPLE ALGORITHMS 
THAT ARE COMPETING AGAINST EACH 
OTHER, WE CAN DEBUTS SAMPLING. IT'S 
A SOLUTION TO THE MUMMY ARMBANDITY 
PROBLEM, WHICH IS MORE OF A ISSUE 
WHERE YOU HAVE MULTIPLE CHOICES 
TO MAKE AND TRYING TO MAXIMIZE YOUR 
REWARD OVER TIME. AND THE DIAGRAM 
SHOWS THREE DIFFERENT ARMS AND GIVEN 
ENOUGH INFORMATION, IT OPTIMALLY 
FINDS THE BEST. WHICH IS, I THINK, 
THE ONE ON THE RIGHT IN BLUE. HERE'S 
AN EXAMPLE OF OUR CONTEXT OBSERVERS 
WHICH IS REALLY WHAT MAKES OUR ALGORITHM 
SPECIAL. OVER TIME, WE'RE GAINING 
MORE EVIDENCE SO THE TRANSACTIONS 
ARE KIND OF SHOWN ON THE THE UPPER 
LEFT. AS WE HAVE MORE TRANSACTIONS, 
WE CAN LEARN ABOUT CATEGORY PREFERENCES, 
LIKE A BUNNY CATEGORY WHICH IS THE 
BLENDED PRODUCTS. AT THE BOTTOM, 
VEGETARIAN CONTEXT OVER. SO AS WE 
LEARN MORE ABOUT A CUSTOMER FROM 
THEIR INTERACTION WITH THE SYSTEM, 
WE'RE UPDATING OUR BELIEF ABOUT 
THOSE PARTICULAR CONTEXTS. WHAT 
YOU SEE HERE IS SOMEBODY WHO MAYBE 
IS LEANING TOWARD VEGETARIAN. HIGH 
CONFIDENCE. WHILE THEY DON'T LIKE 
THE BLENDED PRODUCTS OR AT LEAST 
NOT SHOWING A LOT OF ACTIVITY IN 
THAT AREA. SO OUR POTENTIAL FOR 
THE PRODUCTS ARE THEN UPRANKED OR 
DOWN RASPINGED BASED ON OUR CONTEX 
OBSERVERS. WITH THAT, I WILL TURN 
IT OVER TO BRYAN TO TALK ABOUT THE 
JOURNEY A LITTLE MORE AS WELL AS 
THE FUTURE DEEPER. >> THANKS. HEY, 
EVERYBODY. FIRST, THANKS. IT'S A 
REAL PLEASURE WORKING WITH THIS 
CREW. ONE THING THAT'S GREAT ABOUT 
DEEP BREW IS THE PEOPLE YOU GET 
TO MEET ALONG THE WAY AND THE THINGS 
YOU GET TO DO WHEN YOU REACH THIS 
STAGE. I'LL GIVE YOU A BIT OF A 
TOUR OF THE JOURNEY. IT WILL BE 
REALLY QUICK. THREE PHASES. PHASE 
ONE, BUILDING CREDIBILITY WITH AZURE 
AND WITH YOUR PLATFORM. SO IN ORDER 
TO DO ANYTHING WITH MACHINE LEARNING, 
YOU HAVE TO UNLEARN HOW TO TALK 
ABOUT HOW GREAT THIS THING WILL 
BE AND TO FOCUS STRICTLY ON SECURITY. 
SO WE HAD A MANY, MANY MEETINGS 
TO TALK TO PEOPLE AND SAY, BASICALLY 
YOUR MIDDLE NAME IS SECURITY FROM 
HERE ON OUT. THANKS TO EVERYBODY 
MAKING SURE YOUR DATA IS SECURE 
AND YOU'RE DOING EVERYTHING YOU 
CAN TO MAKE SURE YOU'RE BEING A 
RESPONSIBLE CUSTODIAN OF PEOPLE'S 
DATA. MAKE SURE YOUR PROCESS IS 
GREAT SO DATA SCIENTISTS DO WHAT 
THEY DO. ONE PLEASURE OF MY JOB 
IS TO SEE HOW THE CULTURE'S CHANGED 
AT STARBUCKS AND SEAT GETTING TO 
SEE THE DATA SCIENTISTS EMPOWERED 
TO HANDLE REAL WORLD PROBLEMS. HERE 
PHASE THREE, AT BUILD. DIGGAL PEN 
HAD OUTLINED BOARDS AND DRIVE THROUGHS 
AND MOBILE APPS. THIS ALLOWS US 
TO HAVE SOME OF THE BEST CONVERSATIONS 
AND THOSE ARE THE WHAT IF CONVER 
CONVERSATIONS AND WHY COULDN'T CAN 
HE SOME WHENEVER I THINK ABOUT MACHINE 
LEARNING, I THINK ABOUT THE GREAT 
BRAND STARBUCKS HAS, THE GREAT DATA 
WE HAVE AND THE FACT WE HAVE THE 
GREAT PARTNERS GIVING UNBELIEVABLE 
SERVICE. TO A CERTAIN EXTENT WEB 
WONDER WHY WE CAN'T REPLICATE THAT 
IN THE MOBILE APP. EVERY ONE OF 
US IN THIS ROOM HAS A LITTLE INNER 
DIALOGUE ABOUT COFFEE IN THEIR BRAINS. 
THAT'S A THING WE HAVE IN COMMON. 
STARBUCKS IS VERY PROUD THAT THAT 
INNER DIALOGUE IS OFTEN WHERE CAN 
I GET MY CONDUCTION OR HOW QUICKLY 
CAN I GET MY FAVORITE DRINK? MACHINE 
LEARNING ALLOWS US TO START TO THINK 
HOW WE DO A BETTER JOB OF AS SOON 
AS THAT INNER DIALOGUE TICKS OFF, 
BOY, I'M GOING TO GET THIS THING 
JUST THE WAY I WANT IT AND I'LL 
GET IT WHERE I WANT IT. SO SHAMELESS 
PLUG HERE IS THAT IN SEATTLE, UBER 
AND STARBUCKS LINKED UP AND YOU 
ARE ABLE TO HAVE STARBUCKS DELIVERED 
TO WHEREVER YOU ARE AND IT'S A GREAT 
STEP. WE WANT TO GO FURTHER. SO 
DEEP BREW ALLOWS US TO START TO 
THINK ABOUT SOME COOL THINGS. LIKE 
QUICK ORDER. THE MOBILE APP IS FANTASTIC, 
SERVING MILLIONS EVERY DAY. THE 
MODEL WE HAVE SERVE UP RECOMMENDSES, 
MULTIPLE MILLIONS OF RECOMMENDATIONS 
EACH DAY AT A FRACTION OF A SECOND 
AND EVERYBODY'S HAPPY ABOUT IT. 
THERE'S A QUESTION. LIKE WHY WOULDN'T 
WE MAKE IT SUPER EASY? WHY WOULDN'T 
WE KNOW EXACTLY WHAT YOU WANT AND 
WHEN YOU WANT IT? COULD BE WE PREDICT 
WHEN YOU'LL WANT YOUR NEXT LATTE? 
PURPLE FISH IS A VARIANT OF THAT 
AVAILABLE TO START TO EXPERIMENT 
WITH IT. THE MOBILE APP AND EVERYTHING 
MACHINE LEARNING DOES CAN BE TESTED 
WITH DATA THOROUGHLY AND QUICKLY. 
WE'RE INTO A RAPID TESTING CYCLE 
WHERE AMAZING THINGS CAN HAPPEN. 
WE CAN LET THE CUSTOMERS VOTE ON 
WHAT THEY WANT. SO IT'S AN EXCITING 
TIME FOR THAT CUSTOMER EXPERIENCE 
IN THE MOBILE APP. WE CAN GO OTHER 
THINGS TOO, LIKE I DON'T KNOW HOW 
MANY OF YOU HAVE DRIVEN PAST A STARBUCKS 
AND YOU SEE THE DRIVE-THRU LINE 
IS FILLED. YOU JUST PASS IT OFF. 
LET'S GO TO THE NEXT PLACE OR I'LL 
WAIT. WELL, WHEN YOU HAVE A MACHINE 
LEARNING MODEL. INFRASTRUCTURE IN 
PLACE AND YOU CAN TALK TO ANY TOUCH 
POINT, WE CAN START TO TAKE A MORE 
AGGRESSIVE STANCE HOW TO MARKET 
DURING PEAK HOURS. SO WHEN WE HAVE 
A LOT OF TRAFFIC AND THE STORE IS 
CLOGGED UP, WE CAN NOW HAVE ALL 
THE MARKETING FROM THE MOBILE APP 
TO THE MENU BOARDS TO THE DRIVE-THRU 
RECOMMEND PRODUCTS THAT ARE QUICK 
TO PREPARE. WE CAN HELP YOU GET 
YOUR COFFEE A LITTLE FASTER. THAT 
THROUGHPUT IS A WIN FOR EVERYBODY. 
EVERYBODY SHOULD HAVE WHAT THEY 
WANT, OF COURSE. WE CAN SURE PITCH 
THINGS MOST CONVENIENT TO GET THAT 
LINE MOVING QUICKLY . AND ANOTHER 
THING IS THAT STARBUCKS HAS GREAT 
VALUES AND WE WANT TO MAKE SURE 
WE'RE BEING GOOD CUSTODIANS OF THE 
PLANET HERE. SO THERE'S AN ASPIRATION 
TO HAVE ZERO WASTE IN THE FOOD WE 
BUY AND WHAT'S LEFT OVER AT THE 
END OF THE DAY. EVERYTHING THEY 
ARE WORKING ON ALLOWS US TO HAVE 
VISIBILITY INTO EXACT INVENTORY 
POSITION FOR ALL OUR STORES. IF 
YOU KNOW YOUR INVENTORY POSITIONS 
AND KNOW WHAT THE SALES ARE IN THAT 
MOMENT, YOU CAN CREATE A PREDICTION 
OF WHAT WILL BE LEFT OVER AT THE 
END OF THE DAY AND POTENTIALLY WASTE. 
SO COMBINE THAT WITH THE ABILITY 
TO TALK TO ANY TOUCH POINT AND YOU 
CAN START TO PROMOTE PRODUCTS TO 
LEAD AN INVENTORY ZERO POSITION. 
WE WANT PEOPLE TO BE HAPPY. THEY 
SHOULD GET WHATEVER THEY WANT. AGAIN, 
THE MACHINE CAN BE LIKE THE BEST 
MANAGER THAT'S OUT THERE THAT SAYS, 
HEY, WE HAVE EXTRA OF X PRODUCT. 
LET'S ALL PUSH THIS A LITTLE BIT. 
SO IN OUR HEADS, WE SEE A VERY BRIGHT 
FUTURE FOR TACKLING IMPORTANT CUSTOMER-FACING 
BUSINESS-FACING PROBLEMS THROUGH 
MACHINE LEARNING. AND HE TALKS ABOUT 
BEING ABLE TO DEPLOY THE RIGHT INFRASTRUCTURE. 
AZURE'S CERTAINLY DONE A GOOD JOB 
FOR US IN THAT FRONT. I WON'T SUGAR 
COAT IT. BUT THERE HAVE BEEN MANY 
LATE NIGHTS AND THERE HAVE BEEN 
PROBLEMS WE NEEDED TO FIGHT THROUGH. 
THIS IS THE BUILD CONFERENCE, AND 
YOU ALL KNOW THAT CAN BE IT FUN 
STUFF. THERE'S NOT A MOMENT OF THAT 
I WOULD WANT TO TURN BACK. AZURE 
ALSO HAS A BEAUTIFUL SORT OF APINGLE 
TO IT THAT IT'S PRETTY DETERMINISTIC. 
ONCE YOU HEAD DOWN A PATH AND LEARN 
THE CAPABILITIES AND WHEN YOU SEE 
THE THINGS BEING UNLOCKED AT A CONFERENCE 
LIKE THIS, YOU KNOW YOU'LL BE ABLE 
TO GET YOUR JOB DONE. IT'S JUST 
A MATTER OF TIME. SOMETIMES YOU 
NEED TO WORK THROUGH IT. THAT'S 
FUN. THE MOST, I THINK, IMPORTANT 
THING IS THAT WE ARE IN THIS NEW 
WORLD WHERE IT'S A BATTLE FOR TRUST. 
STARBUCKS IS A TRUSTED BRAND AND 
A BATTLE FOR CONVENIENCE AND COMPANIES 
THAT ARE ABLE TO DEPLOY MACHINE 
LEARNING MODELS CAN SCALE, REACH 
CUSTOMERS, AND SOLVE PROBLEMS THAT 
ALLOW US TO COMPETE IN THIS WORLD 
OF CONVENIENCE AND TRUST. WITH THAT, 
I WILL TURN IT OVER TO SOME QUESTIONS. 
FOR ANYBODY WHO HAS THEM. THANKS. 
[APPLAUSE] >> QUESTIONS. >> (INAUDIBLE) 
PROGRESSIVE WEB APP [INAUDIBLE] . 
WAS THAT CONSCIOUSLY A DECISION 
YOU MADE, OR JUST HAVEN'T GOTTEN 
TO IT? >> THE NATIVE APP WAS THE 
FIRST PLACE WE STARTED. WE'RE ACTUALLY 
JUST SORT OF IN THE MIDST OF THE 
WEB APP. THAT WILL BE THE NEXT APPLICATION 
FOR US TO THINK THROUGH. WE STARTED 
THE JOURNEY. THAT WASN'T ACTUALLY, 
FRANKLY, EVEN A THING AT THAT POINT. 
YES. >> HOW DID YOU APPROACH JUST 
TO FIND THE [INAUDIBLE] BUILDING 
BREW KIT AND BUILDING THIS CUSTOM 
FRAMEWORK? HOW DID YOU KIND OF APPROACH 
THAT? >> FROM AN ROI PERSPECTIVE, 
AND WHY MAYBE IF I COULD EXTEND 
YOUR QUESTION A LITTLE BIT. SO INSTEAD 
OF JUST MAYBE USING A VENDOR OR 
SOMETHING OFF THE SHELF VERSUS WHY 
WE BUILD THE CAPABILITY OURSELVES, 
SO I THINK A CONSCIOUSLY AND YOU 
HEARD BRYAN TALK ABOUT TRUST. I 
THINK ONE THING FOR US, THERE ARE 
A COUPLE THINGS. ONE, WE REALLY 
INVESTED A LOT OF ENERGY OVER THE 
LAST TWO YEARS OR SO TO ACTUALLY 
BUILD UP THE DATA SCIENCE PRACTICE 
INTERNALLY AT STARBUCKS. AND OVER 
THE LAST FEW YEARS, WE'VE TAKEN 
DIFFERENT FOR YAYS INTO PARTNERING 
WITH DIFFERENT VENDORS I THINK WE 
ALL RECOGNIZE THE WORLD IS CHANGING 
JUST IN TERMS OF DATA PRIVACY AND 
SECURITY AND, AGAIN, I'LL KIND OF 
REFLECT BACK ON WHAT HE SAID ABOUT 
THE MOST IMPORTANT THING TO US IS 
THE RELATIONSHIP WITH A CUSTOMER 
AND WHETHER IT'S OFFLINE OR DIGITAL. 
FOR US, AS WE THINK ABOUT HOW DO 
WE ENSURE OUR CUSTOMERS CAN ENTRUST 
US WITH THEIR DATA AND HOW WE TREAT 
THE DATA AS YOU THINK ABOUT IT, 
SO THERE'S TWO WAYS, THEN, TO THINK 
ABOUT IT. ONE WOULD BE THE BEST 
WAY TO ENSURE THE TRUST IS IF YOU 
KEEP IT YOURSELF. AND YOU BUILD 
THE CAPABILITIES INTERNALLY. I THINK 
THE SECOND THING I'D SAY ON THAT 
IS THE WORST THING WE COULD DO, 
PROBABLY ONE OF THE MOST SIGNIFICANT 
ASSETS WE HAVE AS AN ORGANIZATION 
IS OUR CUSTOMER INFORMATION AND 
THE KNOWLEDGE WE HAVE ABOUT IT. 
IT'S NOT JUST WHAT THESE GUYS HAVE 
LEARNED THROUGH THE MODELS WE BUILT 
BUT IT'S ALSO THE POWERING THAT 
WITH JUST WHAT THE MARKETING TEAM 
AND OTHER TEAMS ORGANIZATIONALLY 
INTUITLY KNOW ABOUT OUR CUSTOMER 
BEHAVIOR. I TALKED A BIT ABOUT THE 
MOD IDIOSYNCRASYIZATION. THE REALITY 
SUE COULD GO GIVE YOUR KATE DID 
YOU TO A THIRD PARTY. REALITY IS 
IT WILL NEVER BE AS GOOD AS WHAT 
YOU COULD BUILD INTERNALLY. VIEW 
IT AS A COMPETITIVE ADVANTAGE FOR 
US. IT'S ALSO, AGAIN, TO DOUBLE 
DOWN HOW SERIOUSLY WE TAKE OUR CUSTOMERS' 
PRIVACY AND SECURITY AROUND THEIR 
DATA. GOOD QUESTION. >> [INAUDIBLE] 
>> IT YEAH. IT'S A GOOD QUESTION. 
FEEL FREE TO JUMP IN. WE'VE DONE 
A GOOD JOB OF BUILDING THE INFRASTRUCTURE. 
I THINK THE WAY I WOULD VIEW THAT, 
THEN, IS THE REALITY IS THAT THE 
PLATFORM IS THERE. THE ALGORITHMS 
ARE THERE. YOU CAN RUN A DIFFERENT 
COUNTRY'S DATA THROUGH IT. NOW I 
THINK THEY'RE PROBABLY SOME NUANCES. 
AS I JUST DISCUSSED AROUND WHAT 
YOU UNDERSTAND ABOUT FROM A MARKETING 
PERSPECTIVE AND WHAT YOU INTUITIVELY 
KNOW AND CUSTOMERS IN JAPAN ARE 
DIFFERENT FROM CUSTOMERS IN CHINA 
AND IN THE U. S. SO THAT'S WHERE 
THE SCIENCE COMES IN A BIT. I THINK 
THE GOOD NEWS IS THAT WE BUILT THE 
PLATFORM. I THINK AS BRYAN HAD TALKED 
ABOUT SORT OF THE BLOOD, SWEAT AND 
TEARS, THAT WAS A LOT OF THE HEAVY 
LIFTING, GETTING THE INFRASTRUCTURE 
IN THE RIGHT PLACE. NOW WE CAN THINK 
HOW YOU MIGHT EASILY EXTEND OR EXPAND 
TO OTHER MARKETS. THERE ARE OF COURSE 
CONDITIONS TO THAT, CERTAIN MARKETS 
WHERE THE DATA NEEDS A STAY IN A 
PARTICULAR GEO LIKE IN CHINA. I 
THINK WE'RE PRETTY WELL POSITIONED 
RIGHT NOW. I THINK IT'S REALLY JUST 
ABOUT BUILDING THE PARTNERSHIPS 
WITH SOME OF THE OTHER COUNTRIES 
TO HOW YOU MIGHT TAKE THE PLATFORM 
AND BUILD RECOMMENDATIONS RELEVANT 
FOR THEIR CUSTOMERS IN THOSE MARKETS. 
>> I THINK I'LL ALSO ADD THE BREW 
KIT FUNCTIONALITY, NOW WE CAN DEPLOY 
IN MINUTE. ESSENTIALLY WE CAN NOW 
ISOLATE SERVICES TO REGIONS AND 
LOCATIONS AND DEPLOY BASED ON THOSE 
LOCATIONS WHERE WE HAVE THE FLEXIBILITY 
OF THE SHARE NOTHING OR SHARE EVERYTHING. 
IT'S BEEN A HUGE STEP. AS LONG AS 
THE SERVICE IS AVAILABLE, WE CAN 
DEPLOY IT. >> SO YOU'VE TALKED A 
LOT ABOUT [INAUDIBLE] LEARNING FROM 
THE USER. [INAUDIBLE] . [INAUDIBLE] 
>> YOU KNOW, I'LL TAKE IT. TELL 
ME TO SHUT UP IF YOU WANT OR SAY 
SOMETHING ELSE. I THINK THE WAY 
I THINK ABOUT IT, YOU KNOW, I THINK 
THE OTHER THING, SO THE THING WE 
DIDN'T TALK ABOUT IT, RIGHT, WAS 
HOW WE'RE REALLY STARTING TO THINK 
THROUGH BUILDING A CUSTOMER 360-DEGREE 
VIEW AND HOW WE LOOK AT MULTIPLE 
DATA SOURCES. SO THE WORK HIS TEAM 
ON THE TECH SIDE IS DOING ISN'T 
JUST ENABLING THE INFRASTRUCTURE 
BUT ALSO FRANKLY HELPING US TO SEAMLESSLY 
BUILD DATA INTEGRATIONS. WHETHER 
IT'S A LOT OF THE DATA WE TALKED 
ABOUT HERE OR AS YOU START TO LACK 
AT OTHER DATA SIGNALS. SO HOW DO 
WE TART TO LACK AT UNSTRUCTURED 
DATA THROUGH YELP OR GOOGLE OR GLASS 
DOOR WHERE YOU'RE PARSING SOCIAL 
DATA AND HOW YOU CAN START TO, I 
THINK TO YOUR POINT, WHERE YOU CAN 
START TO COLLABORATE ACROSS DIFFERENT 
DATA SETS TO SEE WHERE THERE MIGHT 
BE A PAIN POINT OR PINT OF CUSTOMER 
STRUGGLE OR APP REVIEW. YOU CAN 
START TO THINK ABOUT YOUR OWN JOURNEY 
AS A CUSTOMER WITH STARBUCKS OR 
ANOTHER BRAND. ALL THE EXHAUST YOU 
CREATE FROM A DATA PERSPECTIVE. 
WHAT WE'RE TRYING TO DO IS CHECK 
ALL THAT AND HARVEST IT INTO AZURE 
WITHIN THE DATA LAKE TO START TO 
LOOK ACROSS A LOT OF THE DATA SOURCES 
AND REALLY KIND OF CREATE MORE OF 
THAT HOLISTIC VIEW AND START TO 
EXPOSE SOME OF THOSE PROBLEMS. IT'S 
A GOOD POINT. I THINK THAT'S DEFINITELY 
A JOURNEY WE'RE ON. I'D SAY WE'RE 
PROBABLY ABOUT 50 OF THE WAY THERE 
IN TERMS OF DATA WE WANT TO INTEGRATE 
AND WE'LL KEEP GOING DOWN THAT PATH 
TO START TO PAINT THAT PICTURE INTO 
CUSTOMER BEHAVIOR. >> YEAH. >> [INAUDIBLE] 
>> SO FOR COLD START. YOU DON'T 
HAVE ANY USER HISTORY, PERSONAL 
HISTORY. THEN WE DEBUTS THE CONTEXT 
AROUND YOUR LOCATION TO PROVIDE 
WISDOM OF THE CROWD-TYPE REGSES. 
IF WE DON'T HAVE ANY NEW PRODUCT 
THAT COMES OUT LIKE LTO OR SEASONAL 
PRODUCT, THE ALGORITHM IS DOING 
SAMPLING ACROSS MULTIPLE CHOICE 
CONTEXT OBSERVERS SO YOU MIGHT ACTUALLY 
GET A RANDOM, YOU KNOW, PICK THAT 
IS MANAGE NOT OPTIMAL PORE YOU. 
IT DOES HAVE AN AMOUNT OF EXPLORATION 
HAPPENING BASED ON THE HIGHER THE 
DOUBT THE MORE IT WILL TRY TO EXPLORE. 
THAT'S THE EXPLORE FOR THEM. >> 
CAN YOU MAKE AN EDUCATED GUESS? 
>> LET THE ALGORITHM DO IT. THERE'S 
MULTIPLE PROBABILITY DISTRIBUTIONS. 
CONTEXT SERVER UNBLENDED. SO IF 
WE EXAMPLE THAT, WE CAN POTENTIALLY 
GET SOMETHING YOU HAVEN'T TRIED 
BEFORE. WE WOULD WANT TO HAVE THE 
SENSE OF NOVELTY BECAUSE IT'S IMPORTANT 
TO TRY TO NEW THINGS. SO THAT'S 
ONE WAY. THE OTHER WAY, WE HAVE 
MARKETING FOLKS. SO THE MARKETING 
FOLKS CAN WORK WITHIN OUR SYSTEM. 
WE CAN UPRANK NEW PRODUCTS, FOR 
EXAMPLE, CLOUD MACCHIATO IF WE NEEDED 
TO. YEP. >> [INAUDIBLE] >> YOU DAWN 
ERWANT TO? >> I DIDN'T KNOW WHO 
ASKED THE QUESTION. >> [INAUDIBLE] 
>> YEAH. SO I THINK FROM A MODEL 
PERSPECTIVE, YES, WE NEEDED TO, 
AS WE WERE TRAINING. WE NEEDED TO 
HAVE AN AMOUNT OF DATA. WE HAD TO 
PULL A LOT OF OUR HISTORY DATA. 
WE ALSO ARE, YOU KNOW, SETTING RETENTION 
POLICIES ON THE DATA. SO THAT WE'RE 
NOT ESSENTIALLY -- YOU KNOW, IT'S 
NOT A FULL-BLOWN LOOK THROUGH FOR 
ALL. PERSONALLY I BELIEVE WE SHOULD 
START DOING MORE AND MORE WITH LESS 
DATA, ESSENTIALLY MAKE THE MODELS 
ALGORITHMS, TUNE THEM AS MUCH AS 
POSSIBLE. THEY'RE DOING A GREAT 
I DON'T KNOW IN THAT DIRECTION. 
BUT TO ANSWER YOUR QUESTION, WE 
WE LOOKED AT ALL HISTORY DATA TO 
ESSENTIALLY ESTABLISH BASELINE CONTEXT 
AND DERIVE THE ALGORITHM FROM THERE. 
>> THANK YOU. >> [INAUDIBLE] >> 
YES. SO WE HAVE EXPONENTIAL SMOOTHING 
THAT ALLOWS US TO CAPTURE THE PREVIOUS 
YEAR AND HAVE THOSE ITEMS WITH SEASONALITY. 
THERE'S ALSO EXPONENTIAL DECAY HAPPENING. 
THE RECENT SENSE OF RESENSEY IN 
THE ALGORITHM. >> [INAUDIBLE] >> 
THAT'S RIGHT. WE HAVE OPTIONS TO 
DO BOTH DISCOUNTED AND NONDISCOUNTED. 
SO WE LET THE ALGORITHM DECIDE BASED 
ON THE USER'S HISTORY. >> SO YOU 
SAID YOU DEBUTS [INAUDIBLE] >> SO 
NOT CURRENTLY. WE HAD AN EXISTING 
VENDOR WHO WOULD GIVE US. WE FIRST 
WANTED TO MAKE SURE WE WERE THE 
VENDOR. ONCE WE ESTABLISHED THE 
BASELINE AND RUNNING AT 100 OF TRAFFIC 
NOW. NOW'S THE TINT TO IMPROVE THE 
ALGORITHMS, TEST NEW THINGS AND 
HAVE IT LEARN THROUGH SAMPLING. 
>> [INAUDIBLE] >> GOOD QUESTION. 
IT WASN'T ME. [LAUGHTER] >> IT WAS 
THE RESULT OF LATE NIGHT CODING 
AND MAYBE SOME BEER WAS INVOLVED. 
YEAH. >> [INAUDIBLE] >> WE'RE NOT 
MARKETERS, SO WE AREN'T GOOD AT 
NAMING THINGS. >> IT WAS THE SAME 
PERSON WHO NAMED DEEP BREW. ONE 
FOR TWO. 50. [LAUGHTER] >> [INAUDIBLE] 
>> NOT CURRENTLY. WE HAVE TO DEBUTS 
THE STARBUCKS REWARDS DATA. SO MEMBER 
DATA. BUT WE'D LIKE TO MOVE, FIGURE 
OUT WAYS TO MOVE IN THE DIRECTION 
TO SOURCE OTHER DATA. >> ACTUALLY, 
CAN YOU GO BACK TO MY EARLIER QUESTION. 
WE HAD A STARBUCKS CARD BEFORE THE 
APP. DO WE HAVE CARD DATA ASSOCIATED 
WITH NOW THE APP DATA? >> YES. SO 
ESSENTIALLY OUR DATA HAS THE TRANSACTION 
DATA IDENTIFIED BASED ON WHATEVER 
THE CHANNEL WAS. HISTORICALLY, YES, 
IF YOU HAVE MULTIPLE CARD -- THAT'S 
WHY I MENTION THE MERGE, ET CETERA. 
IN A LOT OF CASES AS USERS JUGGLING 
THROUGH MULTIPLE CHANNELS, YOU NEED 
TO MAKE SURE THERE'S A SINGLE POINT 
WHERE WE CAN PINPOINT THAT USER 
ACTIVITY. SO HISTORICAL ANNUAL DATA 
ACROSS DIFFERENT CHANNELS. >> HOW 
DO YOU KNOW IF [INAUDIBLE] SUCCESSFUL? 
>> WE MONITOR A LOT OF EVERYTHING 
HAPPENING. WE HAVE KPIS. WE LOOK 
ON OUR DASHBOARDS AND MAKE SURE 
OUR CONVERSION RATES ARE GOOD AND 
OUR TICKET ARE GOOD. THAT'S ALSO 
HOW WE ARE ABLE TO VIEW OUR AV TESTING. 
THAT'S A LOT OF HEALTH CHECKS AS 
WELL TO MAKE SURE THE SYSTEM'S UP 
AND GIVING RECS. >> SO THE [INAUDIBLE] 
I GUESS THE IDEA HERE IS TO FIGURE 
THIS OUT WITHOUT ASKING BUT DO YOU 
EVER ASK? >> DO WE EVER ASK. I THINK 
WHAT WE'D LIKE TO DO IS TAKE, GIVE 
THE CUSTOMERS THE OPPORTUNITY TO 
PUT THEIR PREFERENCES IN THERE. 
SOME OF THAT IS AVAILABLE. WE NEED 
TO DETERMINE WHETHER OR NOT YOU'RE 
REALLY A VEGETARIAN. THEN OVERRIDE 
THE ALGORITHM, RIGHT? BECAUSE YOU 
ONLY CAN BASE OUR BELIEFS BASED 
ON THE DATA. IF THERE'S INSUFFICIENT 
DATA, WE DON'T KNOW. >> THERE'S 
SOME IDEATION B AROUND AND PLENTY 
OF CLAMS HERE, WHETHER STITCH FIX 
OR NETFLIX AND HOW YOU MIGHT GAMEFY 
A DIGITAL EXPERIENCE TO THINK THROUGH 
PREFERENCES FOR YOU TO PLAY FRANKLY 
A MORE ACTIVE ROLE IN THE PRODUCTS 
AND RECOMMENDSES YOU'RE GIVING. 
SO IT'S SOMETHING WE DON'T CURRENTLY 
HAVE BUT SOMETHING WE'RE DISCUSSING 
IN BUILDING OUT CAPABILITIES AROUND 
THAT. >> DID I MENTION WE'RE HIRING? 
[LAUGHTER] >> AND JUST TO HIGHLIGHT 
A COUPLE THINGS. YOU CAN GET A FREE 
BAG OF COFFEE BEANS EVERY WEEK. 
HOLD YOUR APPLAUSE. [LAUGHTER] >> 
SUMMER FRIDAYS. HALF DAYS FRIDAYS 
ALL THROUGH THE SUMMER. AND FRANKLY, 
WE ARE LITERALLY JUST SCRATCHING 
THE SURFACE. I KNOW I SAID IT ONCE 
ALREADY. BUT WE ARE WELL POSITIONED 
RIGHT NOW TO DO SOME INCREDIBLE 
INNOVATION. ALL THE WAY UP FRANKLY 
TO KEVIN JOHNSON, WHO'S A HUGE ADVOCATE 
FOR WHAT OUR TEAM IS DOING AND IT, 
FRANKLY, YOU WANT TO BE ON THAT 
YOUR HONOR WINDOWS SERVERS COME 
TALK TO ME, FIND ME ON LINKEDIN. 
I WON'T GIVE MY CELL HERE. BUT YOU 
CAN FIGURE OUT HOW TO GET A HOLD 
OF ME AND ALSO GO TO OUR CAREERS 
WEBSITE. WE HAVE A FEW ROLES POSTED 
RIGHT NOW. BUT WE'RE EXCITED TO 
BE BUILDING THE TEAM, EXCITED TO 
SHARE WITH YOU TODAY. LIKE I SAID, 
FEEL FREE TO SWING BY THE BOOTH. 
YOU CAN GO EVEN DEEPER AND EXPERIENCE 
THE DRIVE-THRU AND MLP EXPERIENCE 
FOR YOURSELF AND THANK YOU FOR YOUR 
TIME. [APPLAUSE] 
