Lecture 10: ML Testing & Explainability (Full Stack Deep Learning -...

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Ad

Full Stack Deep Learning - UC Berkeley Spring 2021 - Sergey Karayev, Josh Tobin, Pieter Abbeel
Beyond the Black Box:
Testi...

Full Stack Deep Learning - UC Berkeley Spring 2021
Test score < target performance (in school)
2

Full Stack Deep Learning - UC Berkeley Spring 2021
Test score < target performance (real world)
3
How do we know we can tr...

Full Stack Deep Learning - UC Berkeley Spring 2021
What’s wrong with the black box?
4

Full Stack Deep Learning - UC Berkeley Spring 2021
What’s wrong with the black box?
• Real-world distribution doesn’t alwa...

Full Stack Deep Learning - UC Berkeley Spring 2021
How bad is it, really?
6
“I think the single biggest thing holding back...

Full Stack Deep Learning - UC Berkeley Spring 2021
Goal of this lecture
• Introduce concepts and methods to help you, your...

Full Stack Deep Learning - UC Berkeley Spring 2021
Outline
• Software testing

• Testing machine learning systems

• Expla...

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
9

Full Stack Deep Learning - UC Berkeley Spring 2021
Outline
• Software testing
• Testing machine learning systems

• Explai...

Full Stack Deep Learning - UC Berkeley Spring 2021
Software testing
• Types of tests

• Best practices

• Testing in produ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Software testing
• Types of tests
• Best practices

• Testing in produc...

Full Stack Deep Learning - UC Berkeley Spring 2021
Basic types of software tests
• Unit tests: test the functionality of a...

Full Stack Deep Learning - UC Berkeley Spring 2021
Other types of software tests
• Mutation tests

• Contract tests

• Fun...

Full Stack Deep Learning - UC Berkeley Spring 2021
Software testing
• Types of tests

• Best practices
• Testing in produc...

Full Stack Deep Learning - UC Berkeley Spring 2021
Testing best practices
• Automate your tests

• Make sure your tests ar...

Full Stack Deep Learning - UC Berkeley Spring 2021
Best practice: the testing pyramid
• Compared to E2E tests, unit tests ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Controversial best practice: solitary tests
• Solitary testing (or mock...

Full Stack Deep Learning - UC Berkeley Spring 2021
Controversial best practice: test coverage
• Percentage of lines of cod...

Full Stack Deep Learning - UC Berkeley Spring 2021
Controversial best practice: test-driven development
20
Software testin...

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
21

Full Stack Deep Learning - UC Berkeley Spring 2021
Software testing
• Types of tests

• Best practices

• Testing in produ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Test in prod, really?
23
Software testing

Full Stack Deep Learning - UC Berkeley Spring 2021
Test in prod, really?
• Traditional view:

• The goal of testing is to ...

Full Stack Deep Learning - UC Berkeley Spring 2021
The case for testing in production
Bugs are inevitable, might as well s...

Full Stack Deep Learning - UC Berkeley Spring 2021
The case for testing in production
With suﬃciently advanced monitoring ...

Full Stack Deep Learning - UC Berkeley Spring 2021
How to test in prod
27
Software testing
• Canary deployments

• A/B Tes...

Full Stack Deep Learning - UC Berkeley Spring 2021
Test in prod, really.
28
Software testing

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
29

Full Stack Deep Learning - UC Berkeley Spring 2021
Software testing
• Types of tests

• Best practices

• Testing in produ...

Full Stack Deep Learning - UC Berkeley Spring 2021
CircleCI / Travis / Github Actions / etc
• SaaS for continuous integrat...

Full Stack Deep Learning - UC Berkeley Spring 2021
Jenkins / Buildkite
• Nice option for running CI on your own hardware, ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
33

Full Stack Deep Learning - UC Berkeley Spring 2021
Outline
• Software testing

• Testing machine learning systems
• Explai...

Full Stack Deep Learning - UC Berkeley Spring 2021
ML systems are software, so test them like software?
35
Software is…
• ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Common mistakes in testing ML systems
• Only testing the model, not the...

Full Stack Deep Learning - UC Berkeley Spring 2021
Common mistakes in testing ML systems
• Only testing the model, not the...

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
ML Models vs ML Systems
38
ML
Model
Prediction
system
Serving...

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
ML Models vs ML Systems
39
ML
Model
Prediction
system
Serving...

Full Stack Deep Learning - UC Berkeley Spring 2021
Infrastructure tests — unit tests for training code
• Goal: avoid bugs ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
41

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
ML Models vs ML Systems
42
ML
Model
Prediction
system
Serving...

Full Stack Deep Learning - UC Berkeley Spring 2021
Training tests
• Goal: make sure training is reproducible

• How?
• Pul...

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
Training system
ML Models vs ML Systems
44
ML
Model
Serving s...

Full Stack Deep Learning - UC Berkeley Spring 2021
Functionality tests: unit tests for prediction code
• Goal: avoid regre...

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
ML Models vs ML Systems
46
ML
Model
Serving system
Storage an...

Full Stack Deep Learning - UC Berkeley Spring 2021
Evaluation tests
• Goal: make sure a new model is ready to go into prod...

Full Stack Deep Learning - UC Berkeley Spring 2021
Eval tests: more than just your val score
• Look at all of the metrics ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Behavioral testing metrics
• Goal: make sure the model has the invarian...

Full Stack Deep Learning - UC Berkeley Spring 2021
Robustness metrics
• Goal: understand the performance envelope of the m...

Full Stack Deep Learning - UC Berkeley Spring 2021
Privacy and fairness metrics
51
Fairness Definitions Explained (https:/...

Full Stack Deep Learning - UC Berkeley Spring 2021
Simulation tests
• Goal: understand how the performance
of the model co...

Full Stack Deep Learning - UC Berkeley Spring 2021
Eval tests: more than just your val score
• Evaluate metrics on multipl...

Full Stack Deep Learning - UC Berkeley Spring 2021
Surfacing slices
SliceFinder: clever way of computing metrics across
al...

Full Stack Deep Learning - UC Berkeley Spring 2021
Eval tests: more than just your val score
• Maintain evaluation dataset...

Full Stack Deep Learning - UC Berkeley Spring 2021
Evaluation reports
56
Slice Accuracy Precision Recall
Aggregate 90% 91%...

Full Stack Deep Learning - UC Berkeley Spring 2021
Deciding whether the evaluations pass
• Compare new model to previous m...

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
58

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
Training system
ML Models vs ML Systems
59
ML
Model
Storage a...

Full Stack Deep Learning - UC Berkeley Spring 2021
Shadow tests: catch production bugs before they hit users
• Goals:
• De...

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
Prediction
system
Training system
ML Models vs ML Systems
61
...

Full Stack Deep Learning - UC Berkeley Spring 2021
AB Testing: test users’ reaction to the new model
• Goals:
• Understand...

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
63

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
Prediction
system
Training system
ML Models vs ML Systems
64
...

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
Serving system
Prediction
system
Training system
ML Models vs...

Full Stack Deep Learning - UC Berkeley Spring 2021
Labeling tests: get more reliable labels
• Goals:
• Catch poor quality ...

Full Stack Deep Learning - UC Berkeley Spring 2021
ML system
Labeling system
Serving system
Prediction
system
Training sys...

Full Stack Deep Learning - UC Berkeley Spring 2021
Expectation tests: unit tests for data
• Goals:
• Catch data quality is...

Full Stack Deep Learning - UC Berkeley Spring 2021
What kinds of expectations?
69
https://greatexpectations.io/

Full Stack Deep Learning - UC Berkeley Spring 2021
Challenges operationalizing ML tests
• Organizational: data science tea...

Full Stack Deep Learning - UC Berkeley Spring 2021
How to test your ML system the right way
• Test each part of the ML sys...

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
73

Full Stack Deep Learning - UC Berkeley Spring 2021
Outline
• Software testing

• Testing machine learning systems

• Expla...

Full Stack Deep Learning - UC Berkeley Spring 2021
Some definitions
• Domain predictability: the degree to which it is pos...

Full Stack Deep Learning - UC Berkeley Spring 2021
Making models interpretable / explainable
• Use an interpretable family...

Full Stack Deep Learning - UC Berkeley Spring 2021
Making models interpretable / explainable
• Use an interpretable family...

Full Stack Deep Learning - UC Berkeley Spring 2021
Interpretable families of models
• Linear regression

• Logistic regres...

Full Stack Deep Learning - UC Berkeley Spring 2021
What about attention?
79
Explainability
Interpretable
Not explainable

Full Stack Deep Learning - UC Berkeley Spring 2021
Attention maps are not reliable explanations
80
Explainability

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
81

Full Stack Deep Learning - UC Berkeley Spring 2021
Making models interpretable / explainable
• Use an interpretable family...

Full Stack Deep Learning - UC Berkeley Spring 2021
Surrogate models
• Train an interpretable surrogate
model on your data ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Local surrogate models (LIME)
• Pick a data point to explain the
predic...

Full Stack Deep Learning - UC Berkeley Spring 2021
Making models interpretable / explainable
• Use an interpretable family...

Full Stack Deep Learning - UC Berkeley Spring 2021
Data viz for feature importance
86
Explainability
Partial dependence pl...

Full Stack Deep Learning - UC Berkeley Spring 2021
Permutation feature importance
• Pick a feature

• Randomize its order ...

Full Stack Deep Learning - UC Berkeley Spring 2021
SHAP (SHapley Additive exPlanations)
• Intuition:

• How much does the ...

Full Stack Deep Learning - UC Berkeley Spring 2021
Gradient-based saliency maps
• Pick an input (e.g., image)

• Perform a...

Full Stack Deep Learning - UC Berkeley Spring 2021
Questions?
90

Full Stack Deep Learning - UC Berkeley Spring 2021
Making models interpretable / explainable
• Use an interpretable family...

Full Stack Deep Learning - UC Berkeley Spring 2021
Prototypes and criticisms
92

Full Stack Deep Learning - UC Berkeley Spring 2021
Do you need “explainability”?
• Regulators say my model needs to be exp...

Full Stack Deep Learning - UC Berkeley Spring 2021
Can you explain deep learning models?
• Not really

• Known explanation...

Full Stack Deep Learning - UC Berkeley Spring 2021
Conclusion
• If you need to explain your model’s predictions, use an in...

Full Stack Deep Learning - UC Berkeley Spring 2021
Thank you!
96

Share Slideshare

LinkedIn
Facebook
Twitter

Embed

Size (px)

Show related Slideshows at end

WordPress Shortcode

Link

Share
Email

2024 Trend Updates: What Really Wor... by Search Engine Jou... 1063109 views
Storytelling For The Web: Integrate... by Chiara Aliotta 981811 views
Artificial Intelligence, Data and C... by OECD Directorate ... 923520 views
How to Leverage AI to Boost Employe... by SocialHRCamp 392340 views
2024 State of Marketing Report – by... by Marius Sescu 221896 views
Everything You Need To Know About C... by Expeed Software 237729 views

View on Slideshare

1 of 96 Ad

View on Slideshare

1 of 96 Ad