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www.mimeria.com
Don't build
a data science team
Data 2020 Summit, 2018-09-13
Lars Albertsson
www.mimeria.com
1
www.mimeria.com
Many journeys start ...
2
Big Data!
AI!!
Data driven!
Blockchains?
Real-time
analytics!
www.mimeria.com
Many journeys start with data science
3
Big Data!
AI!!
Data driven!
Blockchains?
Real-time
analytics!
www.mimeria.com
The typical results
4
Proof of value
Prototype
Product
ROI
www.mimeria.com
Wrong data scientists?
5
When to use Jaccard
or cosine distance?
How do you implement an
LSTM with Tensorflow?
When do you
terminate an A/B test?
What we asked them
www.mimeria.com
What we asked them What we should have asked?
Wrong data scientists?
6
When to use Jaccard
or cosine distance?
How do you implement an
LSTM with Tensorflow?
When do you
terminate an A/B test?
How to get data from a
PO using email only?
How to recover Hadoop
namenode?
How to debug AWS
"403 permission denied"?
How to get sysadmin to
open firewall from
Jupyter to MySQL?
www.mimeria.com
Size = effort Credits: “Hidden Technical Debt in
Colour = code complexity Machine Learning Systems”,
Google, NIPS 2015
Machine learning products
7
Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
ML
www.mimeria.com
Data science
Machine learning products
8
Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
ML
www.mimeria.com
The data science team
9
ML
www.mimeria.com
Data science hierarchy of needs
Credits: “The data science hierarchy of needs”,
Monica Rogati
10
AI
Deep learning
A/B testing
Machine learning
Analytics
Segments
Curation
Anomaly detection
Data infrastructure
Pipelines
Instrumentation
Data collection
www.mimeria.com
Data science hierarchy of needs
Data science
Credits: “The data science hierarchy of needs”,
Monica Rogati
11
AI
Deep learning
A/B testing
Machine learning
Analytics
Segments
Curation
Anomaly detection
Data infrastructure
Pipelines
Instrumentation
Data collection
www.mimeria.com
The data science team
12
AI
Deep learning
A/B testing
Machine learning
www.mimeria.com
AI first
● Might work once or twice
● Not a sustainable strategy
13
AI
Deep learning
A/B testing
Machine learning
www.mimeria.com
AI first
● Might work once or twice
● Not a sustainable strategy
● Machine learning is difficult
14
AI
Deep learning
A/B testing
Machine learning
Effort
www.mimeria.com
AI first
● Might work once or twice
● Not a sustainable strategy
● Machine learning is difficult
● Low return of investment
15
AI
Deep learning
A/B testing
Machine learning
Value Effort
www.mimeria.com
AI last
● Lots of hanging fruit
○ Push notifications
○ Simple recommendations
○ Risk & forecasting
○ Reporting
○ Product insights
○ Data-driven product development
○ Anomaly detection
○ ...
● High return of investment
● Media attention != business value
16
Analytics
Segments
Curation
Anomaly detection
Data infrastructure
Pipelines
Instrumentation
Data collection
Value Effort
www.mimeria.com
How do we make best use of data scientists?
17
● They need
○ Supporting roles
○ Continuous access to fresh data
○ Feedback from validation, monitoring, ...
● But where, how, from whom?
?
www.mimeria.com
Data engineering
Domain expertise
What do we need?
18
Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
ML
Product management
QA
www.mimeria.com
Data engineering
Data science
Frontend
Domain expertise
What do we want?
19
Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
ML
DevOps /
DataOps
QA
Product management
www.mimeria.com
Data engineering
Domain expertise
Most data-driven products
20
Configuration Data collection
Monitoring
Serving
infrastructure
Feature extraction
Process
management tools
Analysis tools
Machine
resource
management
Data
verification
Product management
www.mimeria.com
How to get to the summit?
21
www.mimeria.com
Service-oriented architectures
● Data lives with services
● Heterogeneous coupling
22
Service Service Service
App App App
Poll
Aggregate
logs
NFS
Hourly dump
Data
warehouse
ETL
Queue
Queue
NFS
scp
DB
HTTP
DB DBDB
www.mimeria.com
Service-oriented organisations
● Teams own services
● Teams own data
23
www.mimeria.com
Data-centric innovation
● Need data from teams
○ willing?
○ backlog?
○ collected?
○ useful?
○ quality?
○ extraction?
○ data governance?
○ history?
24
www.mimeria.com
Data-centric innovation
● Need data from teams
○ willing?
○ backlog?
○ collected?
○ useful?
○ quality?
○ extraction?
○ data governance?
○ history?
● Innovation friction
Value adding Waste
25
www.mimeria.com
Big data - a collaboration paradigm
26
Stream storage
Data lake
Data
democratised
www.mimeria.com
Data pipelines
27
Data lake
www.mimeria.com
More data - decreased friction
28
Data lake
www.mimeria.com
In the lab
One shot
29
www.mimeria.com
In the lab vs in production
One shot Iterative
30
Data lake
www.mimeria.com
Data agility
● Siloed: 6+ months
● Autonomous: 1 month
● Coordinated: days
31
Data lake
∆
∆
Latency?
www.mimeria.com
Data agility
● Siloed: 6+ months
Cultural work
● Autonomous: 1 month
Technical work
● Coordinated: days
32
Data lake
∆
∆
Latency?
www.mimeria.com
What to do with my data scientists?
● Get them out into production
● Pair them with
○ Data engineers
○ Domain experts
○ Product owners
● Invest in processing capabilities
33
www.mimeria.com
Key takeaways
● Machine learning is a team sport
● Solid data processing is necessary
● Learning happens in production
Lars Albertsson, founder of Mimeria
Data-value-as-a-service - tailored data platforms & data pipelines
34
www.mimeria.com
Key takeaways
● Machine learning is a team sport
● Solid data processing is necessary
● Learning happens in production
Lars Albertsson, founder of Mimeria
Data-value-as-a-service - tailored data platforms & data pipelines
35

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