Danse avec les unicorns : la data science en agile, de l'exploration à l'adoption

Conférence par Wassel Alazhar lors de l'Agile Tour Aix-Marseille 2018.

"La data science sort enfin des grottes des scientifiques. Tout le monde est convaincu que cette discipline changera la donne.
Les outils et les plateformes foisonnent et les startups qui ont fait ce pari sont devenues des unicorns.

Cependant, construire des applications qui apportent une réelle valeur ajoutée reste un vrai challenge. Plusieurs projets prometteurs n'ont pas réussi à dépasser le stade du PoC.

L'année dernière, j'ai accompagné mon client dans son aventure avec une "licorne" californienne qui produit une plateforme de Big data analytics et AI.
Dans cette session, nous allons voir comment faire une data science agile pour l'ajout constant de valeur.

On s'intéressera à :

– l'exploration de la valeur métier
– la collaboration entre différents domaines d'expertise (data science, data integration, app development, UX…)
– la qualité du produit dans un domaine exploratoire

Une agilité à tous les étages !"

Transcript

1. Dances with unicorns
   Agile datascience
   from exploration to adoption
   

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2. Un grand merci à nos sponsors et partenaires
   

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3. Oman 11.30 AM
   

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4. Paris 9.45 AM
   

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5. <24h
   From user’s feedback to production
   

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6. One month later...
   

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7. This project has been
   abandoned
   

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8. That’s me!
   Wassel Alazhar
   Consultant, developer, problem solver
   @wasselovski
   https://github.com/jcraftsman
   

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9. Agenda
   ● The full story
   ● What went wrong?
   ● What did we learn?
   ○ How to bring value from datascience
   ○ Explore and build
   ○ Efficient collaboration
   ○ Product quality
   ● Why this talk?
   ● Takeaways
   

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10. The full story
    

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11. A global energy leader
    

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12. A global energy leader
    Produce Deliver SELL
    

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13. A global energy leader
    Produce Deliver SELL
    Sensors everywhere!
    All along the value chain
    

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14. The problem to solve
    Produce Deliver SELL
    Sensors everywhere!
    All along the value chain
    

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15. The problem to solve
    Two new generation power plants
    They are exactly the same
    but...
    Twin A Twin B
    

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16. The problem to solve
    Twin B is way more performant (i.e., makes money)
    Twin A Twin B
    

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17. The solution
    Datascience can
    help identifying
    better operational
    models for the
    power plants
    

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18. The solution
    BIG DATA
    +
    DATA SCIENCE
    =
    MAGIC
    

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19. The partner
    

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20. The partner
    A unicorn is a privately held startup company valued at over
    $1 billion
    

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21. The bill
    But wait…
    How much is that?
    Nevermind.
    It’s all on me!
    It’s called innovation.
    Great!
    

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22. The team
    Data engineers Data scientists App developers
    

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23. To the Silicon valley
    Data engineers Data scientists App developers
    

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24. Week after week… Demo after demo
    

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25. It couldn’t be any better
    

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26. SURPRISE
    Now, it’s all yours!
    All you have to pay
    for is the run.
    Oh!
    No, thanks.
    I’m out of it.
    

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27. Deception
    

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28. What went wrong?
    

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29. Building a software!
    

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30. What was the Problem to solve?
    Do you remember the twin power plants?
    Twin A Twin B
    

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31. Not what we’ve expected...
    Problem solved explained quickly
    No actionable findings
    

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32. Instead we have delivered features!
    Degradation analysis
    Anomaly detection
    The software can detect dust in the steam turbine!
    PCA???
    

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33. Feature
    ≠
    VALUE
    

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34. What did we learn?
    

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35. Happy ending stories...
    Predictive maintenance
    Smart buildings
    Ice detection
    Heating and cooling efficiency
    

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36. Business use case discovery
    Don’t start with a software!
    Explore
    Observe
    Confirm hypothesis or not!
    Discover
    

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37. Business use case discovery
    Don’t explore in a dark lab!
    Get feedback!
    

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38. Business use case discovery
    A python notebook is not a software!
    It’s a tool for a study!
    

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39. From study to product delivery
    Business use case located?
    Build!
    VALUE
    

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40. Product delivery
    Not like this!
    

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41. Wait, what does datascience look like in 2018?
    How would you write a program for puppy recognition?
    

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42. Wait, what does datascience look like in 2018?
    You can:
    ● Try to define what a puppy face is
    ● Code all these rules!
    Or, use Machine learning:
    ● Show a lot of puppy faces examples!
    You don’t need to tell the algorithm what to do.
    All you need is to show it a lot of examples!
    

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43. Wait, what does datascience look like in 2018?
    Take care of your examples (data pipeline)
    Verify the results (predictions)
    

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44. Putting it all together
    Discovery:
    Given a real world pictures sample, would it be possible to
    recognize a puppy face?
    The answer is 86% yes, 13% muffins, 1% unknown.
    Product:
    Play a dog kibble comercial whenever a puppy picture is
    displayed!
    

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45. Explore and build
    Explore:
    ● Gathering data
    ● Cleaning data
    ● Feature engineering
    ● Defining model
    ● Training
    ● Predicting the output
    => Discover what you are able to do
    with your data
    Build:
    ● Data acquisition
    ● Data filtering
    ● Use model configuration
    ● Use model
    ● Training (or use a train set)
    ● Predicting the output
    => Steadily bring value from your
    data
    

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46. Explore and build iteratively
    Explore:
    ● Gathering data
    ● Cleaning data
    ● Feature engineering
    ● Defining model
    ● Training
    ● Predicting the output
    => Discover what you are able to do
    with your data
    Build:
    ● Data acquisition
    ● Data filtering
    ● Use model configuration
    ● Use model
    ● Training (or use a train set)
    ● Predicting the output
    => Steadily bring value from your
    data
    

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47. Explore and build iteratively
    Explore Build
    Business
    use case
    discovery
    Product delivery
    

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48. Product delivery
    You’re not done with datascience!
    They should build together!
    

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49. Building together
    Code review
    When? All the time!
    Who? Everyone!
    Why? Quality, collective
    ownership and joy!
    

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50. Building together
    Pair programming
    When? All the time!
    Who? Everyone!
    Why? Quality, collective
    ownership and joy!
    

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51. Building together
    Mob programming
    When? Whenever you start
    something new or complex.
    Who? Everyone!
    Why? Collective
    intelligence, collective
    ownership, quality and joy!
    

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52. Building together
    TDD
    Let’s be serious!
    When? Whenever you change the
    product’s behaviour.
    Who? Everyone working on the product!
    Why? Collective intelligence,
    collective ownership, quality and joy!
    

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53. Building together
    TDD
    Have you ever met a data
    scientist who write unit tests
    and refactor? I did! :)
    It’s hard to imagine doing TDD
    during an exploratory work though!
    (i.e., when the target observable
    behaviour is not yet defined)
    

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54. Product delivery
    Spikes and user stories
    

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55. Product delivery essentials
    Don’t lose time repeating boring stuff!
    Automate!
    Make data available for everyone!
    Don’t treat your infra like pets!
    Destroy and rebuild!
    Don’t over-engineer though!
    

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56. Product adoption
    Stay close to the users!
    Don’t plan too many features!
    Incorporate feedback!
    

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57. What is agile anyway?
    

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58. Can datascience be agile?
    It’s still true!
    Even for:
    ● Big data
    ● AI
    ● Datascience
    

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59. Why this talk?
    

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60. Myths about datascience
    Well… Things have slightly changed since then… But not that much!
    

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61. Myths about datascience
    

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62. Myths about datascience
    

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63. Unicorns
    

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64. New unicorns - Same old stories
    You should draw your entire
    model before you start coding!
    Open a ticket!
    You need to hire a machine
    learning engineer!
    

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65. Takeaways!
    

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66. Takeaways!
    Make people together!
    Business value discovery => product delivery
    Explore and build iteratively
    Agile is still:
    ● Short feedback
    ● Small increments
    ● Take engineering seriously
    work
    learn
    

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