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

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Dances with unicorns Agile datascience from exploration to adoption

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Un grand merci à nos sponsors et partenaires

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Oman 11.30 AM

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Paris 9.45 AM

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<24h From user’s feedback to production

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One month later...

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This project has been abandoned

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

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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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The full story

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A global energy leader

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A global energy leader Produce Deliver SELL

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

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

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

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

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

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The solution BIG DATA + DATA SCIENCE = MAGIC

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The partner

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

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

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The team Data engineers Data scientists App developers

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To the Silicon valley Data engineers Data scientists App developers

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Week after week… Demo after demo

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It couldn’t be any better

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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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Deception

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What went wrong?

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Building a software!

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

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

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

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Feature ≠ VALUE

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What did we learn?

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

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

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

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

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

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Product delivery Not like this!

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

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

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

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

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

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

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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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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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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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Product delivery Spikes and user stories

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

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What is agile anyway?

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

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Why this talk?

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

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Myths about datascience

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Myths about datascience

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Unicorns

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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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Takeaways!

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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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