Big Data and

Machine

Learning at

Zalando

K s h i t i j K u m a r

V i c e P r e s i d e n t ,

D a t a I n f r a s t r u c t u r e

1 0 - 0 3 - 2 0 1 9

2

WE LOVE FASHION

3

WHAT STARTED AS A

SIMPLE ONLINE

SHOP…

4

…HAS BECOME THE

LEADING EUROPEAN

ONLINE PLATFORM

FOR FASHION

5

W E O F F E R A S U C C E S S F U L AN D C U R AT E D AS S O R T M E N T

HIGHLY

EXPERIENCED category management

CURATED

SHOPPINGwith Zalon

> 500 designers & stylists

> 300,000 articles from

~ 2,000international brands

private labels11

LOCALIZATIONof the assortment

6

PLATFORM STRATEGY

BRANDS CONSUMERS

ENABLER

7

WE DRESS CODE

8

WE ARE CONSTANTLY INNOVATING

CLOUD-BASED,

CUTTING-EDGE

& SCALABLEtechnology solutions

> 2,000employees at

international

tech locations8

HQsin Berlin

help our brand to

WIN ONLINE

9

BIG DATA AT ZALANDO

Business

Intelligence

Machine

Learning

Data

Governance

Data at the

core of

everything

we do

10

A TYPICAL BIG DATA INFRASTRUCTURE

ML Platform

•Explore

•Train

•Serve

•Observe

Data Platform

• Ingestion,

•Metadata,

•Store,

•Process

Business Intelligence

•Data Warehousing

•Visual KPIs

•Trusted datasets

Data Governance

•Data Catalog

•Privacy

•GDPR

11

SOME ML USE CASES AT AN ONLINE RETAILER

12

AN ML-DRIVEN CUSTOMER

EXPERIENCE

13

ML-driven

real-time

reco

engine

People who browsed this style also browsed these other styles…

14

COMPLETE THE LOOK

• Multi-dimensional ML driven

product placement

• Search

• Recommended products

• Complimentary items

• Size (fit)

• Delivery promise

15

THE ML JOURNEY

Explore

Fetch

Prepare

Train Model

Evaluate Model

Deploy to production

Monitor/ Evaluate

Ready the dataServe the models

16

ACHIEVING THE BALANCE TO RUN ML AT SCALE

Exploding new With the needs

17

THE ML PIPELINE – FOR A SINGLE USE CASE

ML Use CaseNotebook/UI

creates workflows

Fetch Data

Extract Features

Prepare Data

Train Model Deploy Model

Serve

Monitor

Evaluate and Feedback

18

WHAT HAPPENS – WITH A COUPLE OF USE CASES

19

AND THE MESS THAT COMES WITH MANY USE

CASES

20

TACKLING THE ML SCALING CHALLENGE

With cost efficiency

The ability to run hundreds of training jobs that are “serverless”. Trainings produce models and infrastructure is automatically shutdown.

3

With safety

The ability to understand metadata at every stage of the ML journey by just describing a training job at the

call of an API.

2

With speed

The ability to compose training jobs, tuning jobs and endpoints with ease, at the call of an API, and with algorithms available out of the box.

1

21

END TO END ML PIPELINE(real-life use case)

22

Productionizing ML: Speed, with simplicity

23

SAFE AND MONITORABLE ML

How is the model endpoint performing?

WHERE WOULD BE LIKE TO BE IN 2020?

A Scalable, Cost-efficient, Flexible Data Infrastructure

Shared Data, Models, Features

Safe, secure data usability, with privacy

Open source, Inner source, best-of-breed vendor tools

We’re hiring!

Big Data and

Machine

Learning at

Zalando

K s h i t i j K u m a r

V i c e P r e s i d e n t ,

D a t a I n f r a s t r u c t u r e

k s h i t i j . k u m a r @ z a l a n d o . d e

1 0 - 0 3 - 2 0 1 9

27

ML pipelines should be safe and understandable

What training job resulted in the deployment?

Which model(s) was deployed?

What instances are the model(s) deployed?

How much traffic routed to which model?