Keynote at the 6th Int’l. Conference on Lean and Agile Software Development, January 22, 2022 Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined "Software 2.0," but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.

1. Agility in
Software 2.0
– Notebook Interfaces
and MLOps with
Buttresses and Rebars
6th Int’l. Conference on Lean and
Agile Software Development
January 22, 2022
RISE Research Institutes of Sweden
CC
BY-NC
2.0
K.
Edblom
Markus Borg
@mrksbrg
mrksbrg.com

2. Agile iterates fast.
Data science moves faster!
(CC
CC BY-NC 2.0 K. Edblom

3. Who is Markus?
• Development engineer, ABB 2007-2010
– Process automation
– Editor and compiler development
• PhD student, LundUniversity 2010-2015
– Requirements engineering and testing
– Traceability, change impact analysis
• Senior researcher, RISE 2015-
– Software engineering for AI/ML

5. Also Markus…
• Member of the board, Swedsoft
– Influence decision makers
– Write comment letters
– Facilitate networking
• Adjunct lecturer, Lund University
– Teaching software engineering

6. Software 2.0
Computational
Notebooks
MLOps
(CC BY-NY-ND 2.0 Flick: *Hajee)

8. Software 2.0?

9. ”a large portion of real-world problems have the
property that it is significantly easier
to collect the data than to
explicitly write the program”
https://medium.com/@karpathy/software-2-0-a64152b37c35
Andrej Karpathy
Director of AI at Tesla
9

10. Karpathy’s Software 2.0
Software 1.0
• Humans write source code
• Other humans comprehend the
source code
Software 2.0
• Humans curate data and specify goals
• Backprop. and gradient descent produces
millions of weights
• Humans cannot comprehend mapping
from input to output

11. “computers’ ability to learn without
being explicitly programmed”
- Arthur Samuel (1959)
11

12. 12
Cars
Car
f(x)
Not cars
Supervised learning

13. Neural
network
YOLO (You Only Look Once) by Redmon et al. (2016)
13

14. CC BY-NC 2.0
Flickr: @andreas_komodromos

15. 15
Another ware!
Hardware
Software
AI
MLware

16. 16

17. 17
Established software quality
assurance no longer sufficient

18. 18
MLware is different
Software engineering practices throughout the lifecycle must evolve
– New types of requirements (explainability, fairness, …)
– New architectures (system, neural networks, …)
– Configuration management (data, models, parameters, …)
– Testing levels (data, model, …)
– Operations (scaling, monitoring, retraining, …)

19. Computational
Notebooks

20. Markus
Borg
Martin
Jakobsson
Johan
Henriksson
20
Oskar
Handmark

21. 21
Data science is not software engineering
Established best practices might not apply
Biggest difference is how experimental it is
• Maximum agility needed to quickly reach insights
CACE principle
• “Changing Anything Changes Everything”

22. 22
Also different peopleware
Data scientists are not software engineers
• and maybe not software developers
• perhaps not even computer scientists
Data scientists master the art of taming data and train models
Analogy: research on development of scientific software

23. 23

24. “Literate programming”
- Donald E. Knuth (1984)
24
Mix source code and explanatory text

25. Computational notebooks
Extended into “literate computing” with
three types of cells
• (Python) Source code
• Explanatory text describing the code
• Visual content
Promotes interaction!
Interpreter runs in the background
Cells can be executed in any order
Very agile, but very messy

26. 26

27. 27
Notebook collaboration pain points
Concurrent editing is confusing
Code management and refactoring
Replicability is low
Productization of a Notebook proof-of-concept is a big step
CHI’20

28. 28
28
Very agile!
Loads of tools!

29. 29

30. 30
30
Jakobsson and Henriksson (2021)
https://lup.lub.lu.se/student-papers/search/publication/9066685
https://github.com/backtick-se/cowait

31. dataset versioning?
dependency management?
deployment?
real-time performance?
version control?
repeatability?
scalability?
monitoring?
feature store? model management?
Data Features Training Model
MLware sounds simple!
31

32. Sculley et al. (2015), Hidden Technical Debt in
Machine Learning Systems
In Proc. of the Advances in Neural Information Processing Systems 28

33. MLOps

34. 34

35. What is
MLOps
anyway?

36. 36
“MLOps is the standardization and
streamlining of machine learning life
cycle management”
As an engineering discipline, MLOps is a set of practices
that combines Machine Learning, DevOps and Data Engineering,
which aims to
deploy and maintain ML systems in production reliably and efficiently.
- Treveil et al., Introducing MLOps, O'Reilley
Media, Inc., 2020.

37. (CC BY 2.0 Flick: Kuhnt)
DevOps with ML specific additions
• Experiment tracking
• Model management
37

38. MLOps
Please!
How?
Fully managed end-to-end
solution by the hyperscalers
Custom-built on-prem pipelines
38

40. 40
Data
Validation
Model
Training
Model
Validation
Packaging Deploy in
Test Env.
Deploy in
Prod. Env.
Training Deployment
Model
Monitoring
Software Development
ML Development
Data
Repos
Code
Repos
ML Ops
Release
Management
40

41. Published April 21, 2021

42. High Risk
Unacceptable
Prohibited:
Social Scoring, Public Facial Detection,
User Manipulation, …
Minimal Risk
Business as usual:
Video games, Camera
Effects, Spamfilters, …
Limited Risk
Increased Transparency:
Chatbots, Deep Fakes,
Emotion Recognition, …
Conformity Assessment:
1) X under product safety regulations
2) Education, employment, healthcare,
immigration, justice, …

43. 43
High-risk AI providers must
• Document internal rigorous engineering activities
• Quality assurance, fairness, traceability, auditability, …
• Pass independent conformance assessment
• National Supervisory Authority
• Monitoring to continuously check compliance
If not? GDPR style punishment…
• Up to 6% global annual turnover!

44. 44

45. Agile iterates fast.
Data science moves faster!
(CC
CC BY-NC 2.0 K. Edblom

46. Agility in
Software 2.0
– Notebook Interfaces
and MLOps with
Buttresses and Rebars
6th Int’l. Conference on Lean and
Agile Software Development
January 22, 2022
RISE Research Institutes of Sweden
CC
BY-NC
2.0
K.
Edblom
Markus Borg
@mrksbrg
mrksbrg.com