From Zero to ML Hero for Underdogs - Amir Tabakovic

Amir Tabakovic, experiens.ai, , Data Science Conference, Belgrade November 2019
From Zero to ML Hero
for Underdogs
Data Science Conference, November 2019
Amir Tabakovic
@TABAKOVIC

About me
Former Head of Market Development at PostFinance
(Focus FinTech)
VP of Business Development at BigML (Machine
Learning Start-up)
Independent strategy consultant, speaker & lecturer
(University of Fribourg, Bern University of Applied
Sciences, ESADE Barcelona, ICEMD Madrid)
Escape Room designer: “learn about AI and master
the challenge”

Data driven products...
Machine Learning
Data Visualisation

Expert Systems vs. Predictive Apps

"When I was a programmer, I was very good at figuring out all the algorithms and
writing them all down. Today, I think I would try to figure out how to program a
computer to learn something."
Eric Schmidt
Quote Source:
https://www.straitstimes.com/tech/ma
chine-learning-is-next-big-thing-in-
programming
Quote Pic:
wikipedia.com

Traditional Expert Systems: Expert + Programer
● Experts know how
the decision /
prediction is made
(domain/business
expertise)
Start
Stop
Step 1
Step 2
Step 4
Step 5 Step 6B
Step 3 Step 6A
yes
no
● Programmers know
how to translate the
expert’s knowledge
into a computer
program that makes
the prediction
(technical expertise)
● Constructing a system requires both (though they
may be the same person)

Predictive Applications: Data + Algorithm
Illustration
Source: BigML

Classical Programming vs. Machine Learning
Expert Systems Predictive Applications
Rules
Data
Answer
Classical
Programming
Answer
Data
Rules
Machine
Learning
your priority nr. 1
in the next decade

How to start? … if you aren’t Amazon

(A bucket of) food for thought
“You don’t make your company successful by buying a bucket of AI. It’s not about
waiving an AI wand - there is no intrinsic value by itself.
It’s about picking the right thing to do with AI.”
Carl Hillier
AI

AMIR TABAKOVIC
Think big
“There is nothing you can touch at Amazon, nothing on the website, nothing in the
business, that is not run by Machine Learning.
Everything you see […] everything is run by an underlying Machine Learning
system.”
General Manager Amazon ML

Start small
Unless your name is:
, , , …
stop acting like you are
THE MASTER OF AI.
Although there’s some experience in the market how to adopt best practices for
big companies with huge budgets and access to extraordinary talent pools,
there’s not much experience for SMEs when it comes to successful adoption of AI.

3 Typical mistakes in AI transformation
Typical mistakes in AI transformation are:
1. cargo cult behaviour
“Our data scientists are useless! All they do is sit around publishing papers.”
2. building data science teams with questionable expertise and fully
disconnected from the rest of the organisation
Super Ninja Jedi Data Scientist
3. focusing on the infrastructure projects dealing with pre-requirements for
AI and delaying involvement with AI like projects until all issues are fixed
“First we need a data lake”

1. Cargo cult

Neural networks take over other ML methods

Reinforcement learning is gaining momentum

2. Roles in AI/ML team when things go wrong...
Super Ninja Jedi
Data Scientist
The Bad Boy
Data Scientist
The Baby Face
Data Scientist
The Interesting
One
Data Scientist
The Rico
Suave
Data Scientist
The Sporty
One
Data Scientist
The “Meet the
Parents”
Data Scientist
Icons made by itim2101 from flaticon.com
Why so many data scientists are leaving their jobs
https://www.kdnuggets.com/2018/04/why-data-scientists-leaving-jobs.html

Roles in AI/ML team
Source: https://hackernoon.com/top-10-roles-for-your-data-science-team-e7f05d90d961
Icons made by itim2101 from flaticon.com
Data Engineer
Analyst
Applied ML Engineer
Decision Maker
“Data Scientist”
Qualitative Expert
Additional PersonnelData Architect
Domain Expert
Software Engineer
Data Collection Specialist
Data Product Manager
UX Designer
Graphic Designer
Ethicist / Legal Expert
( )

3. “Infrastructure first” trap
● You will never be ready for Machine Learning (moving target)
● Data lake ≠ ML data lake (it’s helpful if you know in advance which features
you will need for ML and generate them upfront)
● There will always be some additional data source to integrate
● It’s difficult to think about automation of ML workflows if there are no existing
examples around
● ...

AMIR TABAKOVIC
Life cycle AI/ML Tools (based on Wardley Mapping)
Auto ML
Data Science
Tools
Source: BigML

AMIR TABAKOVIC
Sure, why not?

Automation of AI/ML

Advantages of Auto ML
● Increases productivity by automating repetitive tasks. This enables a data
scientist to focus more on the problem rather than the models.
● Automating the ML pipeline also helps to avoid errors that might creep in
manually.
● AutoML is a step towards democratizing machine learning by making the
power of ML accessible to everybody.
Source: https://heartbeat.fritz.ai/automl-the-next-wave-of-machine-learning-5494baac615f

Alternatives: How to kickstart AI transformation
Recommendation for AI transformation:
1. start small with a pilot project (to gain momentum)
2. build a well integrated in-house AI/ML-team – train the trainers
3. provide broad AI/ML training (for experts, for programers…)
4. develop an AI/ML strategy
5. communicate, iterate, deploy, communicate, iterate, deploy...

Start your AI/ML journey with a smile
NO-GO
POSTPONABLE NO-BRAINER
BRAINER
ROI
(impact / costs)
FEASIBILITY
(incdataavailability/deccomplexity)

Few examples for no-brainers & brainers
No-brainers Brainers
Predicting customer churn Chat-bot
Predicting fraud
(transactions, orders, warranty)
Predicting success of early stage startups
Predicting demand
(propensity to buy, next best product, staffing)
Stock-Trading-bot

We run out of yogurt
Source::https://www.nusgram.com/media/BydPmTLiVL1

From Zero to ML Hero for Underdogs - Amir Tabakovic

From Zero to ML Hero for Underdogs - Amir Tabakovic

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