The April 28, 2021, DSDT Meetup focused on the validation of machine learning models, featuring a presentation by Simon Dagenais from Snitch AI. The agenda included discussions on QA processes, risks associated with ML models, and upcoming meetups scheduled for May, June, and July 2021. Attendees were encouraged to contribute ideas for future topics and participate in community efforts to combat poverty and social exclusion.

1. Data Science | Design | Technology
https://www.meetup.com/DSDTMTL
April
28
2021

2. 2
Data Science | Design | Technology
https://www.meetup.com/DSDTMTL
April
28
Please, don't forget to
mute yourself
(2021)

3. JL Maréchaux
DSDT Co-Organizer
(Google Montreal)
Simon Dagenais
Lead Data Scientist
Snitch AI
https://www.meetup.com/DSDTMTL

4. Agenda
3:45 - 4:00 Arrival & Networking
4:00 - 4:15 News & Intro
4:15 - 5:15 How to QA your ML models
5:15 - 5:30: Virtual Snack & Networking
4
DSDT Meetup - April 28, 2021

5. 5
A special thanks to our contributors…
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Thanks
Merci
The
(virtual)
venue
sponsor
& snacks
The brains
...

6. DSDT Mtl meetup
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322,722 views
DSDT Meetup
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322,722 views
DSDT Meetup
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DSDT
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Virtual Meetups
Until we can do in-person events
again in Montreal…
Past (and future) presentations
available on Slideshare.
http://www.slideshare.net/DSDT_MTL

7. Survey: http://bit.ly/DSDTsurvey2021
Which topics should be considered for 2021 meetups (select all that apply)
7

8. Monthly cadence, on Wednesdays.
Incredible sessions already planned for May, June and July.
Contact us with your expectations & ideas.
ML
Validation
Reinforcement
Learning
Explainable
AI
RNN & Time
Series
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Commodo
April 28
May 26 July 21
What is coming in 2021
June 16
Your ideas,
your meetup.
http://bit.ly/DSDTsurvey2021

9. 9
Yes No Maybe
Going?
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May
26
Data Science | Design | Technology
"Autonomous navigation of stratospheric balloons
using reinforcement learning"
Google Brain
May 26
4:00 pm - 5:30 pm
Based on paper published in Nature on
December 2020
No Maybe
Data Science | Design | Technology

10. 10
“
It's time start a new
collaboration and give
back to the community.
Our donations will help
fight against poverty
and social exclusion.
Let's build a stronger
Greater Montreal
together.
Data Science.
Design.
Technology.
More information soon….

11. How to QA your ML
models
Data Science | Design | Technology 11
Simon Dagenais

12. The genesis of an AI system
12

13. The failure of an AI system
13

14. The end of an AI system
14
How could we have prevented that:
● The model’s performance would not degrade once in
production
● Trust and willingness to pursue efforts would come from
management

15. Why are there no systematic QA
approaches in ML?
15
Afterall, ML models are:
● Subject to unexpected inputs
● Built in relationship with other software components
● Expected to be consistent, reliable and usable

16. How should we perform QA on ML
models?
16
● We should uncover and understand those core and
central functions
● We should gain insights of response to altered inputs
● We should also constantly validate the input to our
model

17. An efficient framework for validation
17
● Deriving feature explainability
● Robustness to random and targeted altered data
● Detecting data drift
● Other tests

18. Feature explainability related tests (1)
18
Risk
Errors due to a complex data pipeline.
Data coming from multiple sources
and API
Test
Many features are unimportant in
creating the prediction
Action
Pruning model and dataset

19. Feature explainability related tests (2)
19
Risk
Model learned erroneous and non-replicable patterns
Test
Weakly correlated features or features with
non-causal relationship with your model have strong
a contribution with the output
Action
● Adversarial training
● Data augmentation

20. Feature explainability related tests (3)
20
Risk
Concept drift
Test
Change in feature importance
through time
Action
● Model re-training
● Learning changes
● Pre-processing

21. Robustness to random and targeted noise
21
Risk
The model’s output varies widely to slight
variations in input.
Test
Evaluate the model’s performance with
random or targeted transformation of
input
Action
Data augmentation, adversarial training

22. Data drift
22
Risk
Evaluate whether the distribution of incoming data is similar to the training data’s
Test
Evaluate whether distribution of feature is similar in training data and production
data
Action
Re-train on non-drifting features, use data that is most similar to in-production
input for training (most recent)

23. Other tests
23
● Data Leakage
● Model Simplification
● Overfitting

24. The alternate fate of an AI system
24
The Data science team builds a robust model
On top of that, stakeholders understand:
● On which basis the model emits its prediction
● The associated risk of using the model
● That proper due diligence was conducted by the team

25. 25
Automated scientific validation for your
ML models in a few clicks, without the
need to become an expert.

26. Questions ?
P.S. : We’re hiring DS!
Data Science | Design | Technology 26
Simon Dagenais

27. Merci / Thank You
@DsdtMtl
Data Science | Design | Technology
(Check for next DSDT meetup at https://www.meetup.com/DSDTmtl)
http://bit.ly/dsdtmtl-in