SlideShare ist ein Scribd-Unternehmen logo
1 von 191
Downloaden Sie, um offline zu lesen
Data-Driven
Product Design
J Christopher Garcia
Chris Hyams
Design
J Christopher Garcia
User Experience Lead, Indeed
2008
2013
114 tests
244 variations
84,000 combinations
Direct observation
can be inefficient
Before and after
doesn't work
Traffic doesn't look like this
Hourly traffic looks like this...
...and daily traffic looks like this
...and monthly traffic looks like this
Local maximum
What we've learned
UI Principles
Clickable items should
look clickable
Users read
a word is worth a
thousand icons
+14%
more search
results pages
BEWARE MULTIPLE
CHANGES
Searches: -2.0%
Job Clicks: -1.7%
Saved jobs: -7.6%
Searches: -1.8%
Job Clicks: -0.8%
Saved jobs: 0%
Really?
Really.
Product
Chris Hyams
SVP, Product & Engineering
I help
people
get jobs.
What's best for the job
seeker?
How do we know what's
best for job seekers?
Test and measure
EVERYTHING
Measurement
1. Log everything
jobsearch index
abredistime
acmetime
addltime
adsc
adsdelay
adsi
badsc
badsi
boostojc
boostoji
bsjc
bsjcwia
bsji
bsjindapplies
bsjindappviews
bsjrev
bsjwia
ckcnt
cksz
counts
ctkage
ctkagedays
dayofweek
dcpingtime
domTotalTime
ds-mpo
dsmiss
dstime
featemp
fj
freekwac
freekwarev
freesjc
freesjrev
frmtime
galatdelay
iplat
iplong
jslatdelay
jsvdelay
kwac
kwacdelay
kwai
kwarev
kwcnt
lacinsize
lacsgsize
lmstime
mpotime
mprtime
navTotTime
ndxtime
ojc
ojclong
ojcshort
ojcwia
oji
ojindapplies
ojindappviews
ojwia
oocsc
page
prcvdlatency
primfollowcnt
prvwoji
prvwojlat
prvwojopentime
prvwojreq
radsc
radsi
recidlookupbudget
rectime
redirCount
redirTime
relfollowcnt
respTime
returnvisit
rojc
roji
rqcnt
rqlcnt
rqqcnt
rrsjc
rrsji
rrsjrev
rsavail
rsjc
rsji
rsused
rsviable
serpsize
sjc
sjcdelay
sjclong
sjcnt
sjcshort
sjcwia
sji
sjindapplies
sjindappviews
sjrev
sjwia
sllat
sllong
sqc
sqi
sugtime
svj
svjnostar
svjstar
tadsc
tadsi
time
timeofday
totcnt
totfollowcnt
totrev
tottime
tsjc
tsjcwia
tsji
tsjindapplies
tsjindappviews
tsjrev
tsjwia
unqcnt
vp
wacinsize
wacsgsize
acmepage
acmereviewmod
acmeservice
acmesession
adclick
adcrequest
adcrev
adschannel
adsclick
adsenseclick
adve
advt
agghttp
aggjira
aggjob
aggjob_waldorf
aggsherlock
aggsourcehealth
agstiming
api
apijsv
apisearch
archiveindex
archiveindex_shingled_test
bin
carclicks
click
clickanalytics
cobrand
dctmismatch
draw
dupepairs
dupepairs_mini
dupepairs_old
dupepairsall
dupepairsall_mini
ejchecker
emilyops
feedbridge
globalnav
googlebot_organic
homepage
impression
indeedapply
jhst
jobalert
jobalertorganic
jobalertsearch
jobalertsponsored
jobexpiration
jobexpiration2
jobexpiration3
jobprocessed
jobqueueblock
jobsearch
jssquery
keywordAd
locsvc
lucyindexermain
mechanicalturk
mindyops
mobhomepage
mobil
mobile
mobileorganic
mobilesponsored
mobrecjobs
mobsearch
mobviewjob
myindeed
myindfunnel
myindpage
myindrezcreate
myindsession
old
opsesjasx
organic
orgmodel
orgmodelsubset
orgmodelsubset90
passportaccount
passportpage
passportsignin
ramsaccess
recjobs
recommendservice
resumedata
resumesearch
rexcontacts
rexfunnel
reximpression
rexsearch
rezSrchSearch
rezalert
rezalertfunnel
rezfunnel
rezjserr
rezsrchrequest
rezview
searchablejobs
seo
session
sjmodel
sponsored
sysadappinfo
sysadapptiming
testndx
testndx1
testndx2
tmp
usrsvccache
usrsvcrequest
viewjob
webusersignin
16 million jobs
100 million UV
3 billion searches
1TB per day
Everything you ever wanted to know
about logging but were afraid to ask
Logrepo: Enabling
Data-Driven Decisions
2. Index data
3. Build tools to
query indexes
[another tech talk]
Tools answer questions
Mobile searches per hour in
JP vs. UK?
Resume creation by country?
Impressions in job alerts by
email domain?
Percent of app downloads from
iOS, Android, Windows?
How quickly does a datacenter take
on traffic after a failover?
Test EVERYTHING
Text
Visual design
Layout
Control flow
New features
Algorithms [also another tech talk]
Everything
5% 5%
grp:bluebtntst0 grp:bluebtntst1
split:
log:
Measure
statistical relevance
p-value
Big changes require less data
Conversion Improvement p-value Trials
10% +30% 0.038 1,000
source: http://go.indeed.com/jyusg
Small changes require more data
Conversion Improvement p-value Trials
10% +30% 0.038 1,000
10% +1% 0.042 750,000
source: http://go.indeed.com/ommgd
Product Principles
There's no such thing as
a no-brainer
-30%
Corollary: if a test fails,
it means the test failed
-30%
+25%
Little things can make
a big difference
organic
sponsored
+34%
Corollary: be skeptical
of big changes
Does color really matter
that much?
a A
a B
a C
+34%
+10%
Are people just bored
of blue?
+29% +22%
Will the effect wear off?
Get it right,
then do it right
Location backfill: job clicks
+8%
Location backfill: search time
+1,500%
Location backfill: performance fix
+1,500%
Location backfill: performance fix
+1,500% +0%
Location backfill: job clicks
+8%
Location backfill: clicks after fix
+8% +9%
Measure everything
(aka, Beware of unintended consequences)
April Fools' 2010
Organic clicks
+1.8%
Job alert signups
-17%
Revenue
+15.5%
Revenue
+15.5%
Job alert signups: top v. bottom
-17% +6%-21%
Corollary: attention is a
zero-sum game
(usually)
Search
Click
Revenue
Email signup
Resume create
...
Relative value of actions
Different for everyone
Location auto-complete
Zero-results page
Zero-results pages
-2.7%
Organic clicks
+0%
Clicks w/ auto-complete
+8%
Keyword ad revenue
+1,410%
Keyword ad revenue
+1,410%
keyword ads
Keyword ad revenue after fix
Measure everything,
every time
Getting Started
Fewer tests
Focus on big wins
Conversion Improvement p-value Trials
10% +30% 0.038 1,000
10% +1% 0.042 750,000
source: http://go.indeed.com/ommgd
Straw man
Determine # of trials in a week
Determine minimum improvement
One new test each week
If success, roll out; if not, kill & move on
What We Get
Quality
Risk tolerance
Culture of measurement
(Empirically)
better products
fin
[@IndeedEng] Data-Driven Product Design

Weitere ähnliche Inhalte

Mehr von indeedeng

@Indeedeng: RAD - How We Replicate Terabytes of Data Around the World Every Day
@Indeedeng: RAD - How We Replicate Terabytes of Data Around the World Every Day@Indeedeng: RAD - How We Replicate Terabytes of Data Around the World Every Day
@Indeedeng: RAD - How We Replicate Terabytes of Data Around the World Every Dayindeedeng
 
Indeed My Jobs: A case study in ReactJS and Redux (Meetup talk March 2016)
Indeed My Jobs: A case study in ReactJS and Redux (Meetup talk March 2016)Indeed My Jobs: A case study in ReactJS and Redux (Meetup talk March 2016)
Indeed My Jobs: A case study in ReactJS and Redux (Meetup talk March 2016)indeedeng
 
Data Day Texas - Recommendations
Data Day Texas - RecommendationsData Day Texas - Recommendations
Data Day Texas - Recommendationsindeedeng
 
Vectorized VByte Decoding
Vectorized VByte DecodingVectorized VByte Decoding
Vectorized VByte Decodingindeedeng
 
[@IndeedEng] Imhotep Workshop
[@IndeedEng] Imhotep Workshop[@IndeedEng] Imhotep Workshop
[@IndeedEng] Imhotep Workshopindeedeng
 
@IndeedEng: Tokens and Millicents - technical challenges in launching Indeed...
@IndeedEng:  Tokens and Millicents - technical challenges in launching Indeed...@IndeedEng:  Tokens and Millicents - technical challenges in launching Indeed...
@IndeedEng: Tokens and Millicents - technical challenges in launching Indeed...indeedeng
 
[@IndeedEng] Large scale interactive analytics with Imhotep
[@IndeedEng] Large scale interactive analytics with Imhotep[@IndeedEng] Large scale interactive analytics with Imhotep
[@IndeedEng] Large scale interactive analytics with Imhotepindeedeng
 
[@IndeedEng] Logrepo: Enabling Data-Driven Decisions
[@IndeedEng] Logrepo: Enabling Data-Driven Decisions[@IndeedEng] Logrepo: Enabling Data-Driven Decisions
[@IndeedEng] Logrepo: Enabling Data-Driven Decisionsindeedeng
 
[@IndeedEng] Boxcar: A self-balancing distributed services protocol
[@IndeedEng] Boxcar: A self-balancing distributed services protocol [@IndeedEng] Boxcar: A self-balancing distributed services protocol
[@IndeedEng] Boxcar: A self-balancing distributed services protocol indeedeng
 
[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctor
[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctor[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctor
[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctorindeedeng
 
[@IndeedEng] Engineering Velocity: Building Great Software Through Fast Itera...
[@IndeedEng] Engineering Velocity: Building Great Software Through Fast Itera...[@IndeedEng] Engineering Velocity: Building Great Software Through Fast Itera...
[@IndeedEng] Engineering Velocity: Building Great Software Through Fast Itera...indeedeng
 
[@IndeedEng] Redundant Array of Inexpensive Datacenters
[@IndeedEng] Redundant Array of Inexpensive Datacenters[@IndeedEng] Redundant Array of Inexpensive Datacenters
[@IndeedEng] Redundant Array of Inexpensive Datacentersindeedeng
 
[@IndeedEng] Building Indeed Resume Search
[@IndeedEng] Building Indeed Resume Search[@IndeedEng] Building Indeed Resume Search
[@IndeedEng] Building Indeed Resume Searchindeedeng
 
[@IndeedEng] From 1 To 1 Billion: Evolution of Indeed's Document Serving System
[@IndeedEng] From 1 To 1 Billion: Evolution of Indeed's Document Serving System[@IndeedEng] From 1 To 1 Billion: Evolution of Indeed's Document Serving System
[@IndeedEng] From 1 To 1 Billion: Evolution of Indeed's Document Serving Systemindeedeng
 

Mehr von indeedeng (14)

@Indeedeng: RAD - How We Replicate Terabytes of Data Around the World Every Day
@Indeedeng: RAD - How We Replicate Terabytes of Data Around the World Every Day@Indeedeng: RAD - How We Replicate Terabytes of Data Around the World Every Day
@Indeedeng: RAD - How We Replicate Terabytes of Data Around the World Every Day
 
Indeed My Jobs: A case study in ReactJS and Redux (Meetup talk March 2016)
Indeed My Jobs: A case study in ReactJS and Redux (Meetup talk March 2016)Indeed My Jobs: A case study in ReactJS and Redux (Meetup talk March 2016)
Indeed My Jobs: A case study in ReactJS and Redux (Meetup talk March 2016)
 
Data Day Texas - Recommendations
Data Day Texas - RecommendationsData Day Texas - Recommendations
Data Day Texas - Recommendations
 
Vectorized VByte Decoding
Vectorized VByte DecodingVectorized VByte Decoding
Vectorized VByte Decoding
 
[@IndeedEng] Imhotep Workshop
[@IndeedEng] Imhotep Workshop[@IndeedEng] Imhotep Workshop
[@IndeedEng] Imhotep Workshop
 
@IndeedEng: Tokens and Millicents - technical challenges in launching Indeed...
@IndeedEng:  Tokens and Millicents - technical challenges in launching Indeed...@IndeedEng:  Tokens and Millicents - technical challenges in launching Indeed...
@IndeedEng: Tokens and Millicents - technical challenges in launching Indeed...
 
[@IndeedEng] Large scale interactive analytics with Imhotep
[@IndeedEng] Large scale interactive analytics with Imhotep[@IndeedEng] Large scale interactive analytics with Imhotep
[@IndeedEng] Large scale interactive analytics with Imhotep
 
[@IndeedEng] Logrepo: Enabling Data-Driven Decisions
[@IndeedEng] Logrepo: Enabling Data-Driven Decisions[@IndeedEng] Logrepo: Enabling Data-Driven Decisions
[@IndeedEng] Logrepo: Enabling Data-Driven Decisions
 
[@IndeedEng] Boxcar: A self-balancing distributed services protocol
[@IndeedEng] Boxcar: A self-balancing distributed services protocol [@IndeedEng] Boxcar: A self-balancing distributed services protocol
[@IndeedEng] Boxcar: A self-balancing distributed services protocol
 
[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctor
[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctor[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctor
[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctor
 
[@IndeedEng] Engineering Velocity: Building Great Software Through Fast Itera...
[@IndeedEng] Engineering Velocity: Building Great Software Through Fast Itera...[@IndeedEng] Engineering Velocity: Building Great Software Through Fast Itera...
[@IndeedEng] Engineering Velocity: Building Great Software Through Fast Itera...
 
[@IndeedEng] Redundant Array of Inexpensive Datacenters
[@IndeedEng] Redundant Array of Inexpensive Datacenters[@IndeedEng] Redundant Array of Inexpensive Datacenters
[@IndeedEng] Redundant Array of Inexpensive Datacenters
 
[@IndeedEng] Building Indeed Resume Search
[@IndeedEng] Building Indeed Resume Search[@IndeedEng] Building Indeed Resume Search
[@IndeedEng] Building Indeed Resume Search
 
[@IndeedEng] From 1 To 1 Billion: Evolution of Indeed's Document Serving System
[@IndeedEng] From 1 To 1 Billion: Evolution of Indeed's Document Serving System[@IndeedEng] From 1 To 1 Billion: Evolution of Indeed's Document Serving System
[@IndeedEng] From 1 To 1 Billion: Evolution of Indeed's Document Serving System
 

Kürzlich hochgeladen

Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionDilum Bandara
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxLoriGlavin3
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxLoriGlavin3
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 

Kürzlich hochgeladen (20)

DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An Introduction
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptxUse of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
Use of FIDO in the Payments and Identity Landscape: FIDO Paris Seminar.pptx
 
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptxThe State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 

[@IndeedEng] Data-Driven Product Design