Building a robust machine learning model is not an easy task. After all, most POCs don't make it into production. And even if they make it into production, you still need to monitor its performance.
How can you build performant, tolerant, stable, predictive models that have known and fair biases? How can you make sure your models yield their value over time and stay performant after your team has deployed them? What are the current practices of model validation (or lack of), how are they flawed, and how could we improve them?
Simon Dagenais from Snitch AI will go through the reasons behind using an efficient validation framework that goes beyond the common metrics used by ML practitioners and why these tests matter when building high-quality models.
Agenda:
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3:45pm - 4:00pm: Arrival & Networking
4:00pm - 4:15pm: News & Intro
4:15pm - 5:15pm: How to QA your ML models
5:15pm - 5:30pm: Virtual Snack & Networking
About the main speaker:
---------------------------------
Simon Dagenais is the Lead Data Scientist at Snitch AI, a machine learning validation tool. Before working on Snitch AI, Simon was a data scientist consultant at Moov AI, the parent company of Snitch AI. During his time as a consultant, he built and deployed custom ML solutions to solve business needs at companies like DRW, Société de Transport de Montréal and Cogeco. He now aspires to solve problems that data science teams will encounter during the course of a ML project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.
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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DSDT Meetup
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DSDT Meetup
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Virtual Meetups
Until we can do in-person events
again in Montreal…
Past (and future) presentations
available on Slideshare.
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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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April 28
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What is coming in 2021
June 16
Your ideas,
your meetup.
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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
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Our donations will help
fight against poverty
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Let's build a stronger
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together.
Data Science.
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Technology.
More information soon….
11. How to QA your ML
models
Data Science | Design | Technology 11
Simon Dagenais
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)
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
26. Questions ?
P.S. : We’re hiring DS!
Data Science | Design | Technology 26
Simon Dagenais
27. Merci / Thank You
@DsdtMtl
Data Science | Design | Technology
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