Modern Data Science practice:
Machine Learning - the only expertise you need
B i g D a t a E x p o 2 0 2 2 . U t r e c h t
Alexey Chaplygin
Chief Technology & Product Officer @ expondo GmbH

• Chief Information Officer @ Reface AI
• Data Science Manager @ PVH Europe
• Software Developer / Data Science / Machine Learning
Engineer @ Booking.com, Vrije Universiteit Amsterdam,
ASML, SAP AG and others
Alexey Chaplygin
Chief Technology & Product Officer @
expondo GmbH

• 120+mln EUR revenue
• 400+ exponDOers
• HQ in Berlin
• Offices in Warshaw, Zielona Góra
(PL), Shanghai and Hong Kong
• Very remote friendly!
Key facts:
Procurement from
400+ partners in
China, Vietnam,
India and EU
Product QA control
Logistics to own
warehouse in
Poland
Own digital
production and
marketing
Sales via own web
platform and
marketplaces
Own customer care
and product
aftercare

Company Values

Modern Data Science Practice
from scratch

Data Science vs Machine Learning
• Neural Networks perform worse on small
datasets
• Neural Networks are smoothing
functions
• Neural Networks are being affected
more by noisy inputs
Findings:
Conclusion:
Stick to XGBoost and Random Forest
Find good Machine Learning experts!

Data Science vs Machine Learning
Neural Networks perform worse on small datasets
Medium size business: 10.000.000 EUR revenue, 100 EUR per customer gives 100.000 sales points per year.
Number of impressions, touch-points and events generated by each customers is 100 times bigger.
Neural Networks are smoothing functions
Neural Networks are being affected more by noisy inputs
If you don't know how to cook them and follow only the bookish approach.

Practical Experiments
Experiment #1 – fit the known function: using gradient decent find
coefficients a, b, c, d
Experiment #2 – find the unknown function: from random set X,
consisting 256 points [0,1], knowing f(X) find the function g(x), that
g(X) = f(X)
Experiment #3 – find the unknown space of function: from random
sets X consisting of n random points [0,1], where n is between 1 and
256, knowing f(X) find coefficients a, b, c, d of the function describing
those points

Experiment #1 – fit known
Experiment #1 – fit the known function: using gradient decent find coefficients a, b, c, d
To make it work:
1. Adam -> RMSProp
2. BatchSize -> 1
3. LearningRate -> gradually from 1 to .01
Error Space:

Experiment #2 – fit unknown
Experiment #2 – find the unknown function: from random set X, consisting 256 points [0,1], knowing f(X) find
the function g(x), that g(X) = f(X)
To make it work:
1. Adam -> RMSProp
2. BatchSize -> 1
3. LearningRate -> .001
Only interpolation!

Experiment #3 – fit them all
Experiment #3 – find the unknown space of function: from random sets X consisting of n random points [0,1],
where n is between 1 and 256, knowing f(X) find coefficients a, b, c, d of the function describing those points
To make it work – classic setup!
Raw Data
Feature Engineering
Extracted Features
Regression Model

Dynamic Pricing
The goal:
For each product, each sales channel in each country
find a function (price-demand elasticity),
that depends on price and [all other data available],
which output is sales density.
Product Master
vector
Product Image
matrix
Sales History
sequence of n vectors
Marketing
constant
Classic stack
Feature Engineering: 2FTE, SQL/Python (pandas)
Modelling: 1FTE, Data Science
Deployment: 1FTE, Python Engineering
Total: 4FTE, 3 disciplines
Machine Learning stack
Modelling: 2FTE, Machine Learning Research
Deployment: 1FTE, Machiner Learning Engineering
Total: 3FTE, 1.5 disciplines

Data Science vs Machine Learning
Data Science:
Prepare the raw data sources,
from each data source manually extract a vector of features with the same key to join,
build a Data Science Model using features as the input,
deploy the model.
Machine Learning:
Prepare the raw data sources,
build a Machine Learning Model, which automatically extracts vectors of features on its the shallow layers,
and maps them onto the target space on its deep layers,
deploy the model.
Machine Learning is Data Science with automated feature engineering!

Why Machine Learning as a core
practice?
Pros:
• Narrow stack
• Shared knowledge, less bus factor
• Machine Learning specialists can usually do Data Science, but not the opposite
• Machine Learning specialists are better coders than Data Scientists
• Industry invests a lot in GPUs, TPUs, mobile "TensorCores" and other hardware accelerators for Machine
Learning
Cons:
• Knowledge is scars, both in management and execution
• Seniority required to keep the same speed and quality of developments and models interpretability

T H A N K Y O U F O R Y O U R T I M E !