Productionizing ML Systems without fear or heroism
Nastasia Saby
@saby_nastasia
Examples of ML Systems I've worked on:
- Predicting breakdowns
- Anomalies detection
- Sales models
- etc
#SupervisedLearning #UnsupervisedLearning
Looking at best practices in software engineering
Data monitoring
Unit tests for data
Data versioning
Looking at best practices in software engineering
Predictive systems have a lot to learn from traditional programming
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- Test code
- Version code
- Monitor code
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But predictive systems are different from traditional programming
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Data
Fonction
Programme
Results
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Data
Fonction
Result
Programme
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Data - models > Code
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- Test code, data and models
- Version code, data and models
- Monitor code, data and models
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Using best practices to constantly add value and be able to maintain a regular pace
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Agile software development principles
"Sustainable development, able to maintain a constant pace"
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Software crafting manifesto
"Not only responding to change, but also steadily adding value"
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Reproducibility
No fear or "heroism"
Our goals
- To add value at a sustainable pace
- Being able to reproduce a bug or a past prediction
#serenity #withoutFear #withoutHeroism
Our solution
Look at best practices from traditional programming, but we must go beyond to take into account the specificities of ML systems
Version data
Why should you version your data?
Data > Code
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Doing it yourself
- year = 2019
- month = 11
- month = 12
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Doing it yourself by saving state or events
- year = 2019
- month = 11
- month = 12
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With a tool
#DeltaLake
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DEMO
Version data
Version models
Version code
=
Reproducibility
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Test data
Data can will change
#PyDeequ, #GreatExpectations
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DEMO
Different strategies to deal with "bad" data
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Monitor data
Why should you monitor your data?
#modelDrift
#dataDrift
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Once upon a time, a virus was born in Wuhan
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How can you protect yourself from model and data drift?
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Retraining
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Monitor retraining
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With retrainings
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Monitor real life
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Monitor data
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Monitor data
- Statistical distances
- Statistical tests
=> Open field in the research area
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NO DEMO
Custom
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Azure Data Drift
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Alibi-detect
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EvidentlyAI
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- Statistical tests => black boxes
- Data drift techniques will be popularized soon (I hope)
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Then what you can do?
Unit tests for data + Model drift detection
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DEMO
Then:
- Offline model drift
- Monitoring real life (business impact)
- Unit tests for data
To monitor model drift
MaltAcademy @saby_nastasia
Looking at best practices from software engineering
Model Drift monitoring
Unit tests for data
Data versioning
Productionizing ML Systems without fear nor heroism
By nastasiasaby
