Slide 32
Slide 32 text
@emgsilva
esilva.net @emgsilva
~110 DevOps Teams ~50 Data
Scientists/ML Engineers + DS
Leadership Team); move to
product-led organization
~70 DevOps Teams (mostly
Engineering & Product, ~15
Data Scientists)
~20 DevOps Teams (mostly
Engineering & Product), No
DS present on teams
~ Microservices Architecture
moving to “Product
Architecture”; most DS
developments runs on GCP
~ Microservices
Architecture; Hadoop cluster
for DS “jobs”
~Service-Oriented
Architecture (groups of
teams own “big services”)
1: Data Science evolution: from project, to teams to discipline
Sociotech
Architecture
State
(Parts)
Problem
(Feedback
loop &
Observation)
(Whole)
Synthesis &
Hypothesis
Generation
(Whole)
Tech
Org
Prod Further growth; becoming
product-led org and need for
DS “everywhere” (complexity
requires DS/AI/ML)
“DS Teams” and central DS
Department doesn’t scale or
match “architecture” &
philosophy of our “product
teams” (x-functional teams
that together improve the
product - “no DS islands”!)
time
~2017 ~2020
Big focus on becoming a
“Retailer Platform”; own
shop becomes second prio.
Growing even more!
“DS projects” don’t scale or
match philosophy for DevOps
Teams (You Build It, You Run
You It, You Love It) + low
maturity of Data Science in
org
Online shop, moving to
become also Retailer
Platform (growing a lot!)
Need DS to improve product
relevant + address
complexity, but no DS people
on teams/org yet
~2012
* Get DS “pioneers” in
* Start DS Projects in
strategic topics
* Start building “DS Tech”
* create “DS Teams & Department”,
working on high potential DS products
* Mature DS Discipline
* stop central DS Department & teams
and move into establishing DS as a
discipline that products can have
(like engineering disciplines)
(...)
(...)
(...)
(...)