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
41. Wait, what does datascience look like in 2018?
How would you write a program for puppy recognition?
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!
43. Wait, what does datascience look like in 2018?
Take care of your examples (data pipeline)
Verify the results (predictions)
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!
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
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
47. Explore and build iteratively
Explore Build
Business
use case
discovery
Product delivery
51. Building together
Mob programming
When? Whenever you start
something new or complex.
Who? Everyone!
Why? Collective
intelligence, collective
ownership, quality and joy!
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!
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)
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!
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!
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
