The document discusses the challenges and strategies for successfully industrializing data science projects, emphasizing the need for clear commitments from businesses and multidisciplinary product teams. It outlines a structured approach to developing data science products, including defining life cycle stages, setting measurable criteria, and ensuring alignment with business goals. Key takeaways include fostering commitment, utilizing continuous delivery, and aiming for modular architecture in data science initiatives.

1. a view from the trenches
INDUSTRIALIZING
DATA SCIENCE

3. Data science projects without a clear
industrialization path are just expensive math

4. The data science product life cycle
EXPERIMENT
PRODUCTIDEA
INDUSTRIALIZE
How to overcome the barrier

5. DATA SCIENTISTDATA ENGINEERDATA ARCHITECT BUSINESS
Key roles for successful data science
DATA TRANSLATOR

6. The anatomy of a data science product
X �y
DATA SCIENTIST
?
DATA TRANSLATOR
𝛌𝛌
MODEL

7. The anatomy of a data science product
𝛌𝛌
MODEL SERVINGDATA
MODEL TRAINING MONITORING
DATA ENGINEER
DATA ARCHITECT
BUSINESS
€
INSIGHT ACTION VALUE

8. From proof-of-concept to product
INDUSTRIALIZEEXPERIMENT
3challenges
BUSINESS TEAMS INFRASTRUCTURE
PRODUCTIDEA
FLEXIBILITY RELIABILITY

9. There is a difference between
interest and commitment
When you’re interested in doing something,
you do it only when it’s convenient.
When you’re committed to something,
you accept no excuses, only results.
~ Ken Blanchard
Get business committed and not just interested

10. P U S H
BUSINESS
The challenge
?
Resources
DATA SCIENCE TEAM

11. DATA SCIENCE TEAM
P U L L
BUSINESS
PRODUCT
MANAGER / OWNER
The solution
BUSINESS CASE, PROJECT, TARGETS, BUDGET, EXPERTISE
Create business pull

12. DATA SCIENCE TEAM
1. Who are our end users?
2. Are we solving their problem?
3. Do they want our solution?
1. Is the solution aligned
with our strategy?
2. Is there a clear business case?
3. Do we have budget?
The solution
BUSINESS
Asking the right questions

13. You build it, you run it
Work in multidisciplinary product teams

14. EXPERIMENT TEAM BUSINESS
The challenge
?
Hand-overs

15. INDUSTRIALIZATION TEAM
The challenge
EXPERIMENT TEAM
?? ?
Hand-overs

16. PRODUCT TEAM BUSINESS
The solution
CONTINUOUS DELIVERY
CONTINUOUS FEEDBACK
Product teams

17. The solution
PRODUCTIDEA
BUSINESS
PRODUCT TEAM
Product teams: evolving composition

18. The solution
𝛌𝛌 𝛌𝛌
𝛌𝛌
𝛌𝛌
Product teams: ability to operate and improve
PRODUCT TEAM

19. And they lived happily ever after
Separately
Aim for a modular architecture

20. DATA ARCHITECT
BUSINESS
𝛌𝛌
MODEL SERVINGDATA
The challenge
?
? ?
Embedding the product

21. DATA INTEGRATION LAYER
𝛌𝛌
MODEL SERVING
The solution
Model-as-a-Service
BUSINESS
SOURCES
CONSUMERS

22. PRODUCTIDEA
A data science product pipeline
1. Agree on the life-cycle stages of a data science product
2. Install stage gates with measurable criteria
3. Establish and assign responsibilities at each stage
4. Align technological roadmap
5. Execute and evaluate

23. Key take-aways
Get business committed and not just interested
Work in multidisciplinary product teams
Aim for a modular architecture
Instate a data science product pipeline

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