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Imagine
How
5 Years from Now
will
predictive applications
be put
in production
Our Goal Today
How are we doing today ?
What is difficult ?
What should be simpler?
What is a predictive application ?
Churn Prevention
Fraud Detection
Demand Forecast
Targeting
Maintenance
Match Making
Ad Bidding
Drug Studies
Pricing
Ranking
This discussion not relevant to all
Churn
Maintenance
Drug Studies Multi-Years
Multi-Years
Multi-Years Weekly
Weekly
Yearly
Bidding Two Weeks Sub-Second
Data Span
Retrain
every …
Score
every…
Yearly
Day
Monthly
Monthly
Production
= Dev
Online Learning
Not just a “model”
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
Data
Collection
Let’s call this a
Predictive Service Specification
How much effort ?
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
20% 30% 25% 5% 5% 15%
Data
Collection
Who Does What ?
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
Data Domain
Engineers
Data AnalystsData ScientistsBusiness Intelligence
Engineers
Huge Variety of Tech
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
Data
Collection
ETL ?
Ad-Hoc?
ETL ?
Ad-Hoc?
ETL ?
SQL ? R ? Python ?
Matlab ?
R ? Python ?
R ? Python ? SAS? Java / Python
Business Rules
Management System
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
From Build to Run
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
?
Input Data Decision
Build Time
Run Time
How People Do that Today ?
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
PMMLETL WebServiceScript/SQL
Data
Collection
A Predictive Service
=
Up to 4 different “Applications" that can run out-of-sync
Some Integrated Per-Platform Approach
in Database
in SAS
in Hadoop/Spark
SQL Commercial Warehouse
+ Scoring UDF
End-to-end integration script
Ad-hoc development
Top Companies invested a lot
Each probably >5M$ in their ML production platform
Reason 1 : Prohibitive Costs kill projects
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
RSQL PythonR
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
SQLETL WebServiceSQL PMML
300K$ 50K$ 200K$100K$
50K$
650K$
Reason 2: Distribution Drift
New behaviour
New product
New competitor
Model stops working as planned
You need to be able to do same week update
Reason 3: Mitigate with Data Hazards
You need to be able to do same week update
Most interesting “Big Data” Sources are fragile
Reason 4: Decide is beyond Predict
Most Interesting Problems Require To Combine
Models + Heuristics + Non-local Optimization
Reason 5: “Suits ready” for scalability
Data
Prep
Domain
Specific
Feature Eng.
Feature Eng. Model(s)
Scoring
/Decision
Your CTO could certainly
maintain it up and running all by himself
Your CTO could certainly
maintain it up and running all by himself
Imagine the Dream Platform
That Would Solve All This
?
Let’s call it Blue Box
New Data
Decision
Feature : Cleansing, Enrich and Merge
Blue Box must be the perfect Data Blending runtime
Feature: Aggregating Data
Raw Events Stream Aggregate State
Consolidating History Must be part of Blue Box
1TB-100TB+ 100MB-1OGB
Feature : External Data Compliant
main
data
enriched main
data
additional
data
e.g. Census,
Map, Etc..
Third Data Data Must Be “In” the Blue Box
Feature : Update Data Service
Smart Lazy Human
A/B Test Support in Blue Box
Decision Ver. A
Decision Ver. B
P D F M S
New
Model
Feature : Programatic Decision
Need for Business Compliant
“Real-Time” Rules in Blue Box
model 1
model 2 model 3
if
combine
with
if proba > 0,63 decision A
else decision B
if proba > 0,79 decision A
else decision B
Feature : Audit and Logs
Smart Lazy Human
?
Blue Box needs to keep track of its decisions and Why
Decision Cause Log
External Data
Advanced Join / Matching
Ad-Hoc Transformation
Python / R / Spark DataFrame transformations
SQL Like Transformations
Scoring Causes / Audit
A/B Test Support
Model Rollback / Versioning
Prediction Log. Stats / Audit
Ad-hoc scoring/decision code/scoring
Open Source
What does Blue Box look like?
?
Interesting /
Potential Open Source Project
Real-Time Entity Update, Management,
Scoring
Open Source PMML Scoring in Java
Oryx: Lambda Architecture built on Spark and
Kafka, with specialisation on real-time machine learning
How will we create the “blue box” ?
?
Specification ? PMML Extension ?
Open Source Framework ?
Hadoop / Spark Specific ?
Thank you !
is blue
Convince decisions makers to make
data their competitive advantage
florian.douetteau@dataiku.comjobs@dataiku.com
Wanna work on
this topic ?
Wanna share your
dream features?

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PREDICTIVE APPLICATIONS PRODUCTION

  • 1. Imagine How 5 Years from Now will predictive applications be put in production Our Goal Today How are we doing today ? What is difficult ? What should be simpler?
  • 2. What is a predictive application ? Churn Prevention Fraud Detection Demand Forecast Targeting Maintenance Match Making Ad Bidding Drug Studies Pricing Ranking
  • 3. This discussion not relevant to all Churn Maintenance Drug Studies Multi-Years Multi-Years Multi-Years Weekly Weekly Yearly Bidding Two Weeks Sub-Second Data Span Retrain every … Score every… Yearly Day Monthly Monthly Production = Dev Online Learning
  • 4. Not just a “model” Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision Data Collection Let’s call this a Predictive Service Specification
  • 5. How much effort ? Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision 20% 30% 25% 5% 5% 15% Data Collection
  • 6. Who Does What ? Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision Data Domain Engineers Data AnalystsData ScientistsBusiness Intelligence Engineers
  • 7. Huge Variety of Tech Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision Data Collection ETL ? Ad-Hoc? ETL ? Ad-Hoc? ETL ? SQL ? R ? Python ? Matlab ? R ? Python ? R ? Python ? SAS? Java / Python Business Rules Management System Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision
  • 8. From Build to Run Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision ? Input Data Decision Build Time Run Time
  • 9. How People Do that Today ? Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision PMMLETL WebServiceScript/SQL Data Collection A Predictive Service = Up to 4 different “Applications" that can run out-of-sync
  • 10. Some Integrated Per-Platform Approach in Database in SAS in Hadoop/Spark SQL Commercial Warehouse + Scoring UDF End-to-end integration script Ad-hoc development
  • 11. Top Companies invested a lot Each probably >5M$ in their ML production platform
  • 12. Reason 1 : Prohibitive Costs kill projects Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision RSQL PythonR Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision SQLETL WebServiceSQL PMML 300K$ 50K$ 200K$100K$ 50K$ 650K$
  • 13. Reason 2: Distribution Drift New behaviour New product New competitor Model stops working as planned You need to be able to do same week update
  • 14. Reason 3: Mitigate with Data Hazards You need to be able to do same week update Most interesting “Big Data” Sources are fragile
  • 15. Reason 4: Decide is beyond Predict Most Interesting Problems Require To Combine Models + Heuristics + Non-local Optimization
  • 16. Reason 5: “Suits ready” for scalability Data Prep Domain Specific Feature Eng. Feature Eng. Model(s) Scoring /Decision Your CTO could certainly maintain it up and running all by himself Your CTO could certainly maintain it up and running all by himself
  • 17. Imagine the Dream Platform That Would Solve All This ? Let’s call it Blue Box New Data Decision
  • 18. Feature : Cleansing, Enrich and Merge Blue Box must be the perfect Data Blending runtime
  • 19. Feature: Aggregating Data Raw Events Stream Aggregate State Consolidating History Must be part of Blue Box 1TB-100TB+ 100MB-1OGB
  • 20. Feature : External Data Compliant main data enriched main data additional data e.g. Census, Map, Etc.. Third Data Data Must Be “In” the Blue Box
  • 21. Feature : Update Data Service Smart Lazy Human A/B Test Support in Blue Box Decision Ver. A Decision Ver. B P D F M S New Model
  • 22. Feature : Programatic Decision Need for Business Compliant “Real-Time” Rules in Blue Box model 1 model 2 model 3 if combine with if proba > 0,63 decision A else decision B if proba > 0,79 decision A else decision B
  • 23. Feature : Audit and Logs Smart Lazy Human ? Blue Box needs to keep track of its decisions and Why Decision Cause Log
  • 24. External Data Advanced Join / Matching Ad-Hoc Transformation Python / R / Spark DataFrame transformations SQL Like Transformations Scoring Causes / Audit A/B Test Support Model Rollback / Versioning Prediction Log. Stats / Audit Ad-hoc scoring/decision code/scoring Open Source What does Blue Box look like? ?
  • 25. Interesting / Potential Open Source Project Real-Time Entity Update, Management, Scoring Open Source PMML Scoring in Java Oryx: Lambda Architecture built on Spark and Kafka, with specialisation on real-time machine learning
  • 26. How will we create the “blue box” ? ? Specification ? PMML Extension ? Open Source Framework ? Hadoop / Spark Specific ?
  • 27. Thank you ! is blue Convince decisions makers to make data their competitive advantage florian.douetteau@dataiku.comjobs@dataiku.com Wanna work on this topic ? Wanna share your dream features?