A talk by Eoin Delahunty which was given at the Dublin ML Summit 2018. He can be reached on Twitter @delahuntyondata

1. @delahuntyondata
Machine Learning:
“It’s the Data Stupid”

2. @delahuntyondata
WHAT I’M GOING TO SPEAK ABOUT
1. A brief history of the Database and what we can learn from
its development in terms of ML.
2. Data as an enabler for ML: What does great look like?
3. Challenges and possible solutions.

3. @delahuntyondata
ASSUMPTIONS
● You know some stuff about data.
● You have at least have a working knowledge of what ML is
and what's involved.
● You probably know more than me!

4. @delahuntyondata
Who's built a ML model?

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Who's written a SQL query?

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A brief history of the Database
and what we can learn from its
development in terms of ML

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The humble Database...

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The Database democratised information…
and made many people rich

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Approx year end share
price:
1992: $0.69
2002: $11.56
2016: $39.10
The growth in value of Oracle is a good approximation for the
growth of database technology

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What does Machine Learning
democratise?

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Machine Learning democratises prediction

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In a world where prediction is cheap,
what does that allow us to do?
➔What existing questions can I get better answers
to?
➔What new questions can I now ask that I wasn't
able to before?
➔What new kinds of data can I analyse that would
otherwise be uneconomic?

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Why is it happening now?

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● Commoditisation of
hardware
Why is it happening now?
● Customer expectations
are being driven higher
● Cloud compute as a
service
● ML is having commercial
impact

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Data as a Moat
“Proprietary
corporate data
is a
competitive
moat”

16. @delahuntyondata
Data as an enabler for ML: What
does great look like?

17. @delahuntyondata
Data Wrangling
Credit: Irisdea
Credit: iStockPhoto

18. @delahuntyondata
One Hot Encoding

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Supervised Learning needs Labelled Data
SELECT
Customer
,CASE WHEN trunc(last_active) > ‘01082018’
THEN 0
ELSE 1
END AS Is_Churn
FROM
fact_customer_activity

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How Machine Learning differs
from Business Intelligence

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Machine learning creates more data; you will still need a
database!

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In the world of BI, the journey ended when the data
had made its way to a reporting or visualisation tool

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In the world of ML, it is much more common that your
intended destination is a customer facing application
Credit: Unsplash

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When It Goes Wrong
BI/Reporting ML/Customer Facing
● Someone is "angry with IT" but
the customer is not directly
impacted.
● Customer experience and the
bottom line can be hit more
quickly and more adversely.

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How Machine Learning differs from
Business Intelligence
In order to successfully navigate the Machine Learning world:
➢ New skills, capabilities and potentially new teams need to be
invested in.
➢ The traditional BI/Data team need to be given the room to
expand, grow their knowledge and develop their capability.

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Machine Learning
Skills and Roles

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When there are Pipes, we need Plumbers

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Data Developer Vs Data Engineer
Data Developer Data Engineer
● ETL ● Distributed computing frameworks
● Closed source tools ● Open source packages
● SQL ● R, Python, Scala, C#
● Batch ● Batch & Real time
● Relational Database ● File Storage
Reference: “Successfully Transitioning Your Team from Data Warehousing to Big Data” - Uli Bethke, Sonra

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Data Scientist

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Product Manager/Owner

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Setting your Team up for Success
Data
Engineer
Product
Manager
Data
Scientist

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Machine Learning
Architecture

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Uber’s Michelangelo
Reference: “Meet Michelangelo: Uber’s Machine Learning Platform” - Uber

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Machine Learning Architecture Components
- “Must Haves”
● Pre-Processing pipeline
Supported, managed Production environment
● Orchestration ● API

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Machine Learning Architecture Components
- “Nice to haves”
● Model repo
● Feature store
● Automated model quality checks
● Versioning
● Auto-deploy; CICD
● Containers
● Hyper parameter optimisation

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Offline Vs Online Models
Offline Model Online Model
● Preparing for the exam by
getting all the content up front
and studying it.
● Learning as each question
comes up and optimising your
exam answers over time.

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As some things change, others
remain the same

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The Basic Tenets of Data Management Still Apply

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What does great look like?
Delivering on your business
outcomes

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Challenges and Possible
Solutions

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General Data Protection Regulation
● Understand what you can and cannot do.
● Just because you can, doesn't mean you should.

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Garbage In, Garbage Out (GIGO)
● Understand your
data quality.
● Be aware of bias.

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Keeping a Product Focus
● Prove out hypothesis to achieve customer
outcome.
● Release value. Otherwise it's an expensive
research project.

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Skills
● ML skills are a hot commodity.
● Develop, attract, retain.
● “One talented engineer is better than 10
average engineers”.

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Culture
● Archaic organisational structures
limit technology adoption.
● "Your margin is my opportunity"
- Jeff Bezos
Credit: Context of Things

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Hype

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Explainability
● Explainability can be difficult particularly
with deep neural networks and can inhibit
adoption.
● Progress has been made:
• Local Interpretable Model-Agnostic
Explanations (LIME)
• Feature importance
• SHAP
Credit: Unsplash

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The Future

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The Future
● The impact we see will spread and evolve.
● It becomes normal.
● ML model building becomes commoditised.
● Applications will continue to be deep and narrow.
ML is currently a differentiator but only for another 5-10 years…

50. @delahuntyondata
“In the 1920s and 30s we imagined steel men walking
around factories holding hammers, and in the 1950s we
imagined humanoid robots walking around the kitchen
doing the housework. We didn't get robot servants - we
got washing machines.”
– Benedict Evans
Extremely dumb
but does one
thing really well.
Deep and Narrow
-> Think Washing Machines not Terminator

51. @delahuntyondata
Takeaways
1. Data is the bedrock of Machine Learning. Get your data in a good
state.
2. Have a clear problem to solve and have a clear set of criteria to
measure same.
3. Keep it stupidly simple, particularly at the start. There is a lot of
value from getting to an 80% accuracy.
• "Start supervised, start small, start with good data" - Danny Lange
4. Build a pipeline (people and tech stack)
5. Measure and iterate
• There will be failures, expectation management is critical to adoption.

52. @delahuntyondata
Shameless Plug
Twitter
https://medium.com/@DelahuntyOnData
Medium

53. @delahuntyondata
THANKS & I WANT YOUR FEEDBACK