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

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

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 !

Wassel Alazhar

December 06, 2018
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Transcript

  1. 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
  2. The problem to solve Two new generation power plants They

    are exactly the same but... Twin A Twin B
  3. The problem to solve Twin B is way more performant

    (i.e., makes money) Twin A Twin B
  4. The bill But wait… How much is that? Nevermind. It’s

    all on me! It’s called innovation. Great!
  5. SURPRISE Now, it’s all yours! All you have to pay

    for is the run. Oh! No, thanks. I’m out of it.
  6. What was the Problem to solve? Do you remember the

    twin power plants? Twin A Twin B
  7. Instead we have delivered features! Degradation analysis Anomaly detection The

    software can detect dust in the steam turbine! PCA???
  8. Business use case discovery Don’t start with a software! Explore

    Observe Confirm hypothesis or not! Discover
  9. Business use case discovery A python notebook is not a

    software! It’s a tool for a study!
  10. Wait, what does datascience look like in 2018? How would

    you write a program for puppy recognition?
  11. 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!
  12. Wait, what does datascience look like in 2018? Take care

    of your examples (data pipeline) Verify the results (predictions)
  13. 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!
  14. 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
  15. 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
  16. Building together Code review When? All the time! Who? Everyone!

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

    Why? Quality, collective ownership and joy!
  18. Building together Mob programming When? Whenever you start something new

    or complex. Who? Everyone! Why? Collective intelligence, collective ownership, quality and joy!
  19. 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!
  20. 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)
  21. 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!
  22. Product adoption Stay close to the users! Don’t plan too

    many features! Incorporate feedback!
  23. 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!
  24. 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
  25. OCTO © 2018 - Reproduction interdite sans autorisation écrite préalable

    67 OCTO Provence recrute ! C’EST AVANT TOUT UN ÉTAT D’ESPRIT START-UP APPUYÉ PAR DES EXPERTISES TECH, AGILE & CHANGE POUR ACCOMPAGNER DIGITALE TRANSFORMATION NOS CLIENTS DANS LEUR Contactez-nous sur [email protected]