The document discusses the challenges and techniques associated with continuous delivery for machine learning (CD4ML), emphasizing the complexities of version control for training data, models, and schemas. It outlines the importance of maintaining model performance while managing multiple evolving data aspects through various pipelines. Additionally, it highlights the need for effective orchestration and monitoring in machine learning environments to ensure successful deployment and integration.

1. 1
Continuous
Intelligence
Keeping your AI Application
in Production
Arif Wider
Emily Gorcenski
NDC Porto, April 23-24, 2020

2. 7000+ technologists with 43 offices in 14 countries
Partner for technology driven business transformation
Barcelona - Madrid - London - Manchester - Berlin - Hamburg - Munich - Cologne

3. #1
in Agile and
Continuous Delivery
100+
books written
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4. ©ThoughtWorks 2020

5. TECHNIQUES
Continuous delivery
for machine
learning (CD4ML)
models
#8
TRIAL
8
©ThoughtWorks 2020

6. TECHNIQUES
Continuous delivery
for machine
learning (CD4ML)
models
#8
TRIAL
8
©ThoughtWorks 2020

7. 7
WHERE
IT ALL
STARTED
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8. 8
Data Scientists
Developers

9. 9
Data Scientists
Developers
prediction
model
Feedback,
requests

10. 10
Data Scientists
Developers
prediction
model
Feedback,
requests
SOUNDS LIKE DEV/OPS?

11. 11
Data Scientists
Developers

12. 12
LET’S DO
CONTINUOUS
DELIVERY!

13. CONTINUOUS DELIVERY PIPELINES
13
Prediction Model Pipeline
Web Application Pipeline

14. NEW CHALLENGES
14

15. NEW CHALLENGES
15
→ How to version control training data?
→ Training data and prediction models don’t fit into Git :-(
→ Model re-training slows down the entire continuous delivery server!
→ Data scientists want to evaluate several solutions at the same time...
→ ...and they use analytics notebooks which are hard to version control!
→ How to unit test data science code that is tied to changing data?
→ How to prevent behaviour changes of the model to break the application?

16. WHAT ARE THE REASONS?
16

17. Continuous Delivery is the ability to get
changes of all types — including new features,
configuration changes, bug fixes and
experiments — into production, or into the
hands of users, safely and quickly in a
sustainable way.
Jez Humble & Dave Farley

18. MANY SOURCES OF CHANGE
18
ModelData Code
+ +
Schema
Sampling over Time
Volume
...
Research, Experiments
Training on New Data
Performance
...
New Features
Bug Fixes
Dependencies
...
Icons created by Noura Mbarki and I Putu Kharismayadi from Noun Project
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

19. 19
Code is a broad concept. It can refer to
the source code of our services and
systems. It can represent our
infrastructure. It can refer to the code
used to access, transform, or prepare
data. It can refer to the code used to
train, validate, and assess models.
Code
HOW DOES CODE
CHANGE VERSION?
Bug fixes
New features
Dependencies
Operating systems
Scope change
Etc.
This axis is the best-understood axis of
version change for machine learning
systems.
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

20. 20
Model
Model here refers to both the modeling
approach (e.g. random forest
classification) and the actual artifact
produced (e.g. a pkl file). A model is a
product of hyperparameters (not varied
during training) and parameters
(computed during training).
HOW DOES A MODEL
CHANGE VERSION?
Research — trying new approaches
Dependencies
New data
Performance improvements
Etc.
This axis is not as well-understood and
machine learning, as a field, is changing so
rapidly that many models can be expected
to be obsolete in short order.
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

21. 21
Data
DATA IS MORE COMPLICATED
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

22. 22
Data
DATA IS MORE COMPLICATED
Random Sample by Becris from the Noun Project, Puzzle by
shashank singh from the Noun Project
Sampling Schema
Data is an assessment of the state of the universe at a point in time. It has a measurement and a shape.
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

23. 23
Sampling
Sampling refers to the values in the data
themselves. We can’t measure everything,
but what we do measure is a sample of
the universe. These values have
properties like support, distribution,
cardinality, etc.
HOW DOES SAMPLING
CHANGE VERSION?
Time—it marches ever on
Extrinsic changes in the universe
Inserts, Deletions, Updates
Etc.
It is very difficult to specify or detect shifts
in “versions” here except by example. We
often think of this as moving forward with
time, but that versions are not necessarily
time-dependent.
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

24. 24
Schema
Schema refers to the shape of our data.
This can be a database schema, e.g. a
table/column/constraint definition, or it
could just refer to shape of the incoming
data (e.g. JSON fields, XML schema, etc).
HOW DOES SCHEMA
CHANGE VERSION?
Software updates
Requirements changes
Migrations
Etc.
It is very difficult to specify or detect shifts
in “versions” here except by example. We
often think of this as moving forward with
time, but that versions are not necessarily
time-dependent.
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

25. 25
ML SYSTEMS HAVE FOUR “VERSION AXES”
Any data-driven service can experience version drift along any of these axes independently or jointly.
Schema
Sampling
Model
Code
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

26. DIFFERENT WORKFLOWS
26
master
DEVELOPERS

27. DIFFERENT WORKFLOWS
27
master
change-max-depth
try-random-forest
©ThoughtWorks 2020
DATA SCIENTISTS

28. MORE TYPES OF PIPELINES
DATA PIPELINE
ML PIPELINE DEPLOYMENT
PIPELINE

29. PUTTING EVERYTHING TOGETHER
29
Data Science,
Model
Building
Training Data
Source Code
+
Executables
Model
Evaluation
Productionize
Model
Integration
Testing
Deployment
Test Data
Model +
parameters
CD Tools and Repositories
DiscoverableandAccessibleData
Monitoring
©ThoughtWorks 2020
Production Data

30. WHAT DO WE NEED IN OUR STACK?
30
Doing CD with Machine Learning is still a hard problem
MODEL
PERFORMANCE
ASSESSMENT
VERSION
CONTROL AND
ARTIFACT
REPOSITORIES
©ThoughtWorks 2020
MONITORING
AND
OBSERVABILITY
DISCOVERABLE
AND
ACCESSIBLE
DATA
CONTINUOUS
DELIVERY
ORCHESTRATION
TO COMBINE
PIPELINES
INFRASTRUCTURE
FOR MULTIPLE
ENVIRONMENTS
AND
EXPERIMENTS

31. WHAT DO WE NEED IN OUR STACK?
31
Doing CD with Machine Learning is still a hard problem
MODEL
PERFORMANCE
ASSESSMENT
VERSION
CONTROL AND
ARTIFACT
REPOSITORIES
©ThoughtWorks 2020
MONITORING
AND
OBSERVABILITY
DISCOVERABLE
AND
ACCESSIBLE
DATA
CONTINUOUS
DELIVERY
ORCHESTRATION
TO COMBINE
PIPELINES
INFRASTRUCTURE
FOR MULTIPLE
ENVIRONMENTS
AND
EXPERIMENTS

32. WHAT DO WE NEED IN OUR STACK?
32
Doing CD with Machine Learning is still a hard problem
MODEL
PERFORMANCE
ASSESSMENT
VERSION
CONTROL AND
ARTIFACT
REPOSITORIES
©ThoughtWorks 2020
MONITORING
AND
OBSERVABILITY
DISCOVERABLE
AND
ACCESSIBLE
DATA
CONTINUOUS
DELIVERY
ORCHESTRATION
TO COMBINE
PIPELINES
INFRASTRUCTURE
FOR MULTIPLE
ENVIRONMENTS
AND
EXPERIMENTS
DEMO

33. DEMO
33
Data Scientist
Develops ML Model
Test and productionize
the model
Deploy to production
servers©ThoughtWorks 2020
Application

34. 34

35. YOUR OBJECTIVE FUNCTION MUST LINK TO
BUSINESS VALUE
35
©ThoughtWorks 2020
NDC Porto (Virtual!), April 23, 2020

36. 3636
THANK YOU!
Arif Wider
awider@thoughtworks.com
Emily Gorcenski
egorcens@thoughtworks.com
©ThoughtWorks 2020
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