The document discusses lessons learned in developing data products and deploying data science projects from lab to production. It covers challenges such as integrating with other teams, managing stakeholders, monitoring models in production, and ensuring projects are supported by the necessary infrastructure, tools, and culture. Recommendations include focusing on clean, small data problems first, adopting DevOps practices like monitoring and pipelines, and improving communication between data scientists and other roles.

1. From Lab to Factory
LessonslearneddevelopingDataProducts
Keynote PyCon Colombia
Feb 2017
peadarcoyle@googlemail.com
All opinions my own

2. Who am I?
Data Scientist at Elevate (Recruitment Startup) and Author
@springcoil
About 5 years in Machine Learning/Data Science
OSS contributor (mostly PyMC3)
Built Ad-hoc analysis, Data Products and Data Pipelines at

4. Why talk about this?

5. Data Moats

6. Roadmap
What is Data Science?
Type of Data Scientists
Deploying Data Science (tech/culture)
Success stories and recommendations
Bigger idea (Change)

7. What IS a Data Scientist?

8. There's a Data Science Spectrum
HT: Sean J. Taylor (Facebook Research Scientist)

9. Data Science to Value
Data doesn't do anything!
People need to make decisions (strategic/ tactical)
Or the product needs to alter someones decision making (Spotify
Discover Weekly, Amazon Recommendations, etc)

10. (Taken from Josh Wills talk - Lab to Factory 2014)
or https://hbr.org/2013/04/two-departments-for-data-succe

12. Expectations and reality

13. Data is the new oil...
But. Data isn't a product. It needs to be turned into one!

14. Data Science is hard
Data and technical problems
Skill set (emotional and tech)
Machine Learning Ops

15. Data Science projects are risky!
Many stakeholders think that data science is just an engineering problem,
but research is high risk and high reward
De-risking the project - how? Send me examples :)
http://www.martingoodson.com/ten-ways-your-data-project-is-going-to-
fail/
https://github.com/ianozsvald/data_science_delivered

16. Success with Data Science
1. People
2. Ideas
3. Things
Source: USAF

18. R and D needs cultural support

19. Your culture needs to avoid the HiPPO (highest paid
persons opinion) to be data-informed or data-
driven.

20. Good cultures
Data democratization
Clear objectives and metrics against objectives
Financial metrics don't always win - sometimes brand wins
See by Martin Goodsonhttp://bit.ly/cultureanddata

21. Some data scientists
do experiments and
build prototypes

22. Production

23. (HT: The Yhat people - )www.yhathq.com

24. Deploying Data Products is hard
Monitoring and Alerting
Deployment
Real world data is tricky (training/ testing)
Interpretability (explaining fraud detection/ad tech)
Feature engineering
Models decay over time

25. What projects work?
Explain existing data (visualization!)
Automate repetitive/ slow processes
Augment data to make new data (Search engines, ML models)
Predict the future (do something more accurately than gut feel )
Simulate using statistics :) (Sports analytics models)

26. "You need data first" - Peadar Coyle
Copying and pasting PDF/PNG data
Getting data in some areas is hard!!
Some tools for web data extraction
Messy APIs without documentation :(

27. Visualise ALL THE THINGS!!
(Relay foods dataset - HT Greg Reda)
Consumer behaviour at a Fast Food Restaurant per year in the USA

29. Augmenting data and using API's

30. Machine Learning

31. Production data/ Real world
HT: Andrew Ng

32. Everyone ETLS
Only 1% of your time will be spent modelling
Data pipelines and your infrastructure matters -
Tools such as Spark, Luigi, Airflow or Dask are important
Monitoring of pipelines. Scalability. Machine provision
Eoin Brazil Talk

33. Success Story (1)
1. Ad Tech project (Media company) -
original process took 10 hours per week of analyst time.
2. Broke down process and produced simple predictive model
3. Data type challenges (unicode in Python 2.7) and business
expectations
4. Now takes less than 30 minutes each week.

34. Success Story (2)
Elevate is a Total Talent Management platform (tools for streamlining
recruitment)
Several models in production - microservices (recommenders, job type
prediction)
Data pipeline (Luigi) necessary for producing features
Lots of challenges about Docker, time, updated data.
Works: MyPy (Python 3.5), code review, testing -
Next: Moving to PySpark for ETL, more pair programming
http://hypothesis.works/

35. Failure
1. Lack of support from tech teams
2. Lack of clearly defined customer
3. No culture of DevOps
4. Management had no clear vision

37. Lessons learned from Lab to Factory
1. Monitoring - data products need evaluation in production
2. Lack of a shared language between software engineers and data
scientists. Pair programming/ Code review are some solutions.
3. To help data scientists and analysts succeed your business needs to be
prepared to invest in tooling. (Pipelines, DevOps, Reproducible)
4. Often you're working with other teams who use different languages -
so micro services can be a good idea (example C#.net app and Python
microservice)

38. How to deploy a model?
Palladium (Otto Group)
Azure
Flask Microservice (DIY) and Docker

39. Dev Ops

41. We need each other!

42. Use small data where possible!!
Small problems with clean data are more important - (Ian Ozsvald)
Amazon machine with many Xeons and 244GB of RAM is less than 3
euros per hour. - (Ian Ozsvald)
Blaze, Xray, Dask, Ibis, etc etc -
"The mean size of a cluster will remain 1" - Matt Rocklin
PyData Bikeshed

43. Focus on the real problem

44. Closing remarks
Dirty data stops projects
Change happens, we need to help businesses adapt or be agile
There are some good projects like Icy, Luigi, etc for transforming data
and improving data extraction
- Google paper
on rule for building ML systems
http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

45. Closing Remarks (2)
Stakeholder management is a challenge too
Send me your dirty data and data deployment stories :)
www.peadarcoyle.com
https://leanpub.com/interviewswithdatascientists

46. A New World

48. Simulate: Rugby games with MCMC
(PyMC3)

49. What is the Data Science process?
Obtain
Scrub
Explore
Model
Interpret
Communicate (or Deploy)

50. Some NLP on the Interviews!

51. What do Data Scientists talk about?
Based on my Interview series!Dataconomy

52. A famous 'data product' - Recommendation engines

54. Credits
http://cdn.yourarticlelibrary.com/wp-content/uploads/2013/12/080.jpg
https://www.flickr.com/photos/82066314@N06/7624218468/sizes/z/