SlideShare a Scribd company logo
www.globalbigdataconference.com
Twitter : @bigdataconf
Global Data Science Conference 2017
Fast, Scalable, Reusable:
A New Perspective on
Production ML/AI Systems
by
Ekrem AKSOY, CTO
Global Data Science Conference 2017
Agenda
• AI/ML in the wild business
• Houston! We have a problem (again)
• Top 5 Ideas to Steal
• Summary
Global Data Science Conference 2017
AI/ML in the wild business
Global Data Science Conference 2017
Seriously ???
*Reused with permission of Vladimir Iglovikov
Global Data Science Conference 2017
AI/ML in the wild business
Houston! We have a problem (again)
• Quotation #1:
“Once we have a great model, we used it X times, than it’s
performance felt down. We do not use it anymore…”
Problem is models get corrupted with time and…data.
i.e. AI/ML has not Data Immunity !!!
Global Data Science Conference 2017
Houston! We have a problem (again)
Global Data Science Conference 2017
Usual Software System:
Stays intact after deployment
(i.e. no functional changes w.r.t. data)
Houston! We have a problem (again)
• Quotation #2:
“We have a great team, they’ve built great models, but we need
to process X million rows per (sec, min, …)”
Problem is tech./infra.
used is not enough.
Global Data Science Conference 2017
Houston! We have a problem (again)
Data Infrastructures getting complicated:
- Too many components
- Diverse characteristics of
data
- Configuration Mgmt.
- Re-engineering of models
- …
Global Data Science Conference 2017
Houston! We have a problem (again)
• Quotation #3:
“We invest into XYZ tool, but we can’t use it effectively,
because we need to export manually each time we need it”
Problem is integrability.
Global Data Science Conference 2017
Houston! We have a problem (again)
- Data Inconsistency (format, etc.)
- Non-standard API’s
- Incompatible API’s
- …
Global Data Science Conference 2017
Houston! We have a problem (again)
• Quotation #4:
“Each time it takes X hrs. to produce results, because we
do it manually/it does not scale”
Problem is scalability.
Global Data Science Conference 2017
Houston! We have a problem (again)
- Bootstrap/Cold start issues
- Data hose coupling/de-coupling
- …
Global Data Science Conference 2017
Top 5 Ideas to Steal
• Idea #1: Use basic DevOps cycle
Global Data Science Conference 2017
, but be careful!
Top 5 Ideas to Steal
• Idea #2: De-couple API/Model
Global Data Science Conference 2017
Top 5 Ideas to Steal
• Idea #3: Use schedulers & containers
Global Data Science Conference 2017
Top 5 Ideas to Steal
• Idea #4: Consider Re-writing (or don’t stick to a framework)
Global Data Science Conference 2017
Top 5 Ideas to Steal
• Idea #5: Automatize!
Global Data Science Conference 2017
Summary
1. Production/Business use of AI/ML is different than Academics
or Competition focus.
2. Focus on how to keep models up and running
3. Remember Data Immunity Problem
4. API/Model decoupling is important
5. Adopt best practices (already established, e.g. DevOps)
6. Automatize
Global Data Science Conference 2017
Shameless Self Promotion
If you want to try out, let me know.
ekrem@hiddenslate.com
Global Data Science Conference 2017

More Related Content

What's hot

Push & Pull History of Data Science in Industry & Academia
Push & Pull History of Data Science in Industry & AcademiaPush & Pull History of Data Science in Industry & Academia
Push & Pull History of Data Science in Industry & Academia
June Andrews
 
DataEngConf SF16 - Three lessons learned from building a production machine l...
DataEngConf SF16 - Three lessons learned from building a production machine l...DataEngConf SF16 - Three lessons learned from building a production machine l...
DataEngConf SF16 - Three lessons learned from building a production machine l...
Hakka Labs
 
Data Science Popup Austin: Conflict in Growing Data Science Organizations
Data Science Popup Austin: Conflict in Growing Data Science Organizations Data Science Popup Austin: Conflict in Growing Data Science Organizations
Data Science Popup Austin: Conflict in Growing Data Science Organizations
Domino Data Lab
 
Dsdt meetup june 5
Dsdt meetup june 5Dsdt meetup june 5
Dsdt meetup june 5
JDA Labs MTL
 
Making Open the Default - Bjorn Brembs
Making Open the Default - Bjorn BrembsMaking Open the Default - Bjorn Brembs
Making Open the Default - Bjorn Brembs
Right to Research
 
The A.I. Avenger Team - The 7 roles that you need
The A.I. Avenger Team - The 7 roles that you needThe A.I. Avenger Team - The 7 roles that you need
The A.I. Avenger Team - The 7 roles that you need
Avi Patchava
 
Data Science Popup Austin: Back to The Future for Data and Analytics
Data Science Popup Austin: Back to The Future for Data and AnalyticsData Science Popup Austin: Back to The Future for Data and Analytics
Data Science Popup Austin: Back to The Future for Data and Analytics
Domino Data Lab
 
How academic research on GitHub has evolved in the last several years
How academic research on GitHub has evolved in the last several yearsHow academic research on GitHub has evolved in the last several years
How academic research on GitHub has evolved in the last several years
Keiko Ono
 
Data Engine
Data EngineData Engine
Data Engine
DevCSI
 

What's hot (9)

Push & Pull History of Data Science in Industry & Academia
Push & Pull History of Data Science in Industry & AcademiaPush & Pull History of Data Science in Industry & Academia
Push & Pull History of Data Science in Industry & Academia
 
DataEngConf SF16 - Three lessons learned from building a production machine l...
DataEngConf SF16 - Three lessons learned from building a production machine l...DataEngConf SF16 - Three lessons learned from building a production machine l...
DataEngConf SF16 - Three lessons learned from building a production machine l...
 
Data Science Popup Austin: Conflict in Growing Data Science Organizations
Data Science Popup Austin: Conflict in Growing Data Science Organizations Data Science Popup Austin: Conflict in Growing Data Science Organizations
Data Science Popup Austin: Conflict in Growing Data Science Organizations
 
Dsdt meetup june 5
Dsdt meetup june 5Dsdt meetup june 5
Dsdt meetup june 5
 
Making Open the Default - Bjorn Brembs
Making Open the Default - Bjorn BrembsMaking Open the Default - Bjorn Brembs
Making Open the Default - Bjorn Brembs
 
The A.I. Avenger Team - The 7 roles that you need
The A.I. Avenger Team - The 7 roles that you needThe A.I. Avenger Team - The 7 roles that you need
The A.I. Avenger Team - The 7 roles that you need
 
Data Science Popup Austin: Back to The Future for Data and Analytics
Data Science Popup Austin: Back to The Future for Data and AnalyticsData Science Popup Austin: Back to The Future for Data and Analytics
Data Science Popup Austin: Back to The Future for Data and Analytics
 
How academic research on GitHub has evolved in the last several years
How academic research on GitHub has evolved in the last several yearsHow academic research on GitHub has evolved in the last several years
How academic research on GitHub has evolved in the last several years
 
Data Engine
Data EngineData Engine
Data Engine
 

Similar to Production use of AI/ML Systems

Architecting for Data Science
Architecting for Data ScienceArchitecting for Data Science
Architecting for Data Science
Johann Schleier-Smith
 
Maintainable Machine Learning Products
Maintainable Machine Learning ProductsMaintainable Machine Learning Products
Maintainable Machine Learning Products
Andrew Musselman
 
(Big) Data (Science) Skills
(Big) Data (Science) Skills(Big) Data (Science) Skills
(Big) Data (Science) Skills
Oscar Corcho
 
Lean Security
Lean SecurityLean Security
Lean Security
Ben Johnson
 
From Lab to Factory: Or how to turn data into value
From Lab to Factory: Or how to turn data into valueFrom Lab to Factory: Or how to turn data into value
From Lab to Factory: Or how to turn data into value
Peadar Coyle
 
Large scale computing
Large scale computing Large scale computing
Large scale computing
Bhupesh Bansal
 
Intel 20180608 v2
Intel 20180608 v2Intel 20180608 v2
Intel 20180608 v2
ISSIP
 
2023-My AI Experience - Colm Dunphy.pdf
2023-My AI Experience - Colm Dunphy.pdf2023-My AI Experience - Colm Dunphy.pdf
2023-My AI Experience - Colm Dunphy.pdf
Colm Dunphy
 
Introducción al Machine Learning Automático
Introducción al Machine Learning AutomáticoIntroducción al Machine Learning Automático
Introducción al Machine Learning Automático
Sri Ambati
 
Misusing MLflow To Help Deduplicate Data At Scale
Misusing MLflow To Help Deduplicate Data At ScaleMisusing MLflow To Help Deduplicate Data At Scale
Misusing MLflow To Help Deduplicate Data At Scale
Databricks
 
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Yael Garten
 
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Shirshanka Das
 
NoSQL (Not Only SQL)
NoSQL (Not Only SQL)NoSQL (Not Only SQL)
NoSQL (Not Only SQL)
Pouria Amirian
 
Continuous Intelligence Workshop
Continuous Intelligence WorkshopContinuous Intelligence Workshop
Continuous Intelligence Workshop
David Tan
 
Ai progress = leaderboards compute data algorithms 20180817 v3
Ai progress = leaderboards compute data algorithms 20180817 v3Ai progress = leaderboards compute data algorithms 20180817 v3
Ai progress = leaderboards compute data algorithms 20180817 v3
ISSIP
 
Big Data And Hadoop
Big Data And HadoopBig Data And Hadoop
Big Data And Hadoop
Ankur Tripathi
 
Intro big data analytics
Intro big data analyticsIntro big data analytics
Intro big data analytics
Hagar Alaa el-din
 
Using MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsUsing MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOps
Weaveworks
 
From DevOps to MLOps: practical steps for a smooth transition
From DevOps to MLOps: practical steps for a smooth transitionFrom DevOps to MLOps: practical steps for a smooth transition
From DevOps to MLOps: practical steps for a smooth transition
Anne-Marie Tousch
 
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
Matt Stubbs
 

Similar to Production use of AI/ML Systems (20)

Architecting for Data Science
Architecting for Data ScienceArchitecting for Data Science
Architecting for Data Science
 
Maintainable Machine Learning Products
Maintainable Machine Learning ProductsMaintainable Machine Learning Products
Maintainable Machine Learning Products
 
(Big) Data (Science) Skills
(Big) Data (Science) Skills(Big) Data (Science) Skills
(Big) Data (Science) Skills
 
Lean Security
Lean SecurityLean Security
Lean Security
 
From Lab to Factory: Or how to turn data into value
From Lab to Factory: Or how to turn data into valueFrom Lab to Factory: Or how to turn data into value
From Lab to Factory: Or how to turn data into value
 
Large scale computing
Large scale computing Large scale computing
Large scale computing
 
Intel 20180608 v2
Intel 20180608 v2Intel 20180608 v2
Intel 20180608 v2
 
2023-My AI Experience - Colm Dunphy.pdf
2023-My AI Experience - Colm Dunphy.pdf2023-My AI Experience - Colm Dunphy.pdf
2023-My AI Experience - Colm Dunphy.pdf
 
Introducción al Machine Learning Automático
Introducción al Machine Learning AutomáticoIntroducción al Machine Learning Automático
Introducción al Machine Learning Automático
 
Misusing MLflow To Help Deduplicate Data At Scale
Misusing MLflow To Help Deduplicate Data At ScaleMisusing MLflow To Help Deduplicate Data At Scale
Misusing MLflow To Help Deduplicate Data At Scale
 
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...
 
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
Strata 2017 (San Jose): Building a healthy data ecosystem around Kafka and Ha...
 
NoSQL (Not Only SQL)
NoSQL (Not Only SQL)NoSQL (Not Only SQL)
NoSQL (Not Only SQL)
 
Continuous Intelligence Workshop
Continuous Intelligence WorkshopContinuous Intelligence Workshop
Continuous Intelligence Workshop
 
Ai progress = leaderboards compute data algorithms 20180817 v3
Ai progress = leaderboards compute data algorithms 20180817 v3Ai progress = leaderboards compute data algorithms 20180817 v3
Ai progress = leaderboards compute data algorithms 20180817 v3
 
Big Data And Hadoop
Big Data And HadoopBig Data And Hadoop
Big Data And Hadoop
 
Intro big data analytics
Intro big data analyticsIntro big data analytics
Intro big data analytics
 
Using MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOpsUsing MLOps to Bring ML to Production/The Promise of MLOps
Using MLOps to Bring ML to Production/The Promise of MLOps
 
From DevOps to MLOps: practical steps for a smooth transition
From DevOps to MLOps: practical steps for a smooth transitionFrom DevOps to MLOps: practical steps for a smooth transition
From DevOps to MLOps: practical steps for a smooth transition
 
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
Big Data LDN 2018: HOW AUTOMATION CAN ACCELERATE THE DELIVERY OF MACHINE LEAR...
 

Recently uploaded

DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docxDATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
SaffaIbrahim1
 
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicineIntelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
AndrzejJarynowski
 
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
apvysm8
 
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma TranscriptUniversity of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
soxrziqu
 
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
Timothy Spann
 
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Kaxil Naik
 
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Walaa Eldin Moustafa
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Sm321
 
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
wyddcwye1
 
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
v7oacc3l
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
bmucuha
 
The Ipsos - AI - Monitor 2024 Report.pdf
The  Ipsos - AI - Monitor 2024 Report.pdfThe  Ipsos - AI - Monitor 2024 Report.pdf
The Ipsos - AI - Monitor 2024 Report.pdf
Social Samosa
 
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
mkkikqvo
 
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
ElizabethGarrettChri
 
writing report business partner b1+ .pdf
writing report business partner b1+ .pdfwriting report business partner b1+ .pdf
writing report business partner b1+ .pdf
VyNguyen709676
 
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
nyfuhyz
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
jitskeb
 
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
ihavuls
 
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
hyfjgavov
 
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
a9qfiubqu
 

Recently uploaded (20)

DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docxDATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
 
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicineIntelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
 
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
 
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma TranscriptUniversity of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
 
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
 
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
 
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
 
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
 
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
 
The Ipsos - AI - Monitor 2024 Report.pdf
The  Ipsos - AI - Monitor 2024 Report.pdfThe  Ipsos - AI - Monitor 2024 Report.pdf
The Ipsos - AI - Monitor 2024 Report.pdf
 
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
 
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
 
writing report business partner b1+ .pdf
writing report business partner b1+ .pdfwriting report business partner b1+ .pdf
writing report business partner b1+ .pdf
 
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
 
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
 
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
 
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
 

Production use of AI/ML Systems

  • 2. Fast, Scalable, Reusable: A New Perspective on Production ML/AI Systems by Ekrem AKSOY, CTO Global Data Science Conference 2017
  • 3. Agenda • AI/ML in the wild business • Houston! We have a problem (again) • Top 5 Ideas to Steal • Summary Global Data Science Conference 2017
  • 4. AI/ML in the wild business Global Data Science Conference 2017 Seriously ??? *Reused with permission of Vladimir Iglovikov
  • 5. Global Data Science Conference 2017 AI/ML in the wild business
  • 6. Houston! We have a problem (again) • Quotation #1: “Once we have a great model, we used it X times, than it’s performance felt down. We do not use it anymore…” Problem is models get corrupted with time and…data. i.e. AI/ML has not Data Immunity !!! Global Data Science Conference 2017
  • 7. Houston! We have a problem (again) Global Data Science Conference 2017 Usual Software System: Stays intact after deployment (i.e. no functional changes w.r.t. data)
  • 8. Houston! We have a problem (again) • Quotation #2: “We have a great team, they’ve built great models, but we need to process X million rows per (sec, min, …)” Problem is tech./infra. used is not enough. Global Data Science Conference 2017
  • 9. Houston! We have a problem (again) Data Infrastructures getting complicated: - Too many components - Diverse characteristics of data - Configuration Mgmt. - Re-engineering of models - … Global Data Science Conference 2017
  • 10. Houston! We have a problem (again) • Quotation #3: “We invest into XYZ tool, but we can’t use it effectively, because we need to export manually each time we need it” Problem is integrability. Global Data Science Conference 2017
  • 11. Houston! We have a problem (again) - Data Inconsistency (format, etc.) - Non-standard API’s - Incompatible API’s - … Global Data Science Conference 2017
  • 12. Houston! We have a problem (again) • Quotation #4: “Each time it takes X hrs. to produce results, because we do it manually/it does not scale” Problem is scalability. Global Data Science Conference 2017
  • 13. Houston! We have a problem (again) - Bootstrap/Cold start issues - Data hose coupling/de-coupling - … Global Data Science Conference 2017
  • 14. Top 5 Ideas to Steal • Idea #1: Use basic DevOps cycle Global Data Science Conference 2017 , but be careful!
  • 15. Top 5 Ideas to Steal • Idea #2: De-couple API/Model Global Data Science Conference 2017
  • 16. Top 5 Ideas to Steal • Idea #3: Use schedulers & containers Global Data Science Conference 2017
  • 17. Top 5 Ideas to Steal • Idea #4: Consider Re-writing (or don’t stick to a framework) Global Data Science Conference 2017
  • 18. Top 5 Ideas to Steal • Idea #5: Automatize! Global Data Science Conference 2017
  • 19. Summary 1. Production/Business use of AI/ML is different than Academics or Competition focus. 2. Focus on how to keep models up and running 3. Remember Data Immunity Problem 4. API/Model decoupling is important 5. Adopt best practices (already established, e.g. DevOps) 6. Automatize Global Data Science Conference 2017
  • 20. Shameless Self Promotion If you want to try out, let me know. ekrem@hiddenslate.com Global Data Science Conference 2017