Cnvrg webinar continual learning

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Learn why continual learning is important, and how to use it in your machine learning models to improve accuracy. You can download the full webinar here: https://info.cnvrg.io/continual-learning-webinar

Tecnología

Continual Learning
WEBINAR
How to use continual learning in your ML models
Yochay Ettun, CEO
yochze@cnvrg.io

whoami
• Developer/Data scientist => CEO
• cnvrg.io – built by data scientists, for data scientists to help teams:
• manage, build and automate machine learning
• bridge science and engineering

agenda
• Introduction to Continual Learning
• Why do we need continual learning?
• An ML Pipeline with Continual Learning
• AutoML
• Deployment
• Monitoring
• Live Demo

Continual learning (CL) is the ability of a model to learn continually from a
stream of data, updating the model in production to maintain performance
and relevancy

Why Continual Learning? Data is changing

December 2017 February 2018
Bitcoin price
19K
6K

MNIST ML Pipeline with Continual Learning
- OCR application to identify numbers from handwritten text
- User base is growing, and our model should support new users

DATA Data validation AutoML & HPO Model Validations Model Deployment
Predictions
MNIST ML Pipeline with Continual Learning
MonitoringCleaning & Labeling

MNIST ML Pipeline with Continual Learning
DATA Data validation AutoML & HPO Model Validations Model Deployment
PredictionsMonitoringCleaning & Labeling

• Data is changing -> Models are changing
• Select your algorithm space
• For example, for vision: VGG/Inception/ResNet
• For each algorithm, specify its range of hyperparameters
automl in your continual learning pipeline

An idea of AutoML for Computer Vision using Deep Learning

• Prepare to manage a whole lot more models.
• Track everything!
• Algorithm, hyperparameters, code version, data version, metrics
automl in your continual learning pipeline

• ML Ops
• Automate your machine learning infrastructure and experimentation using
cloud services and Kubernetes!
automl in your continual learning pipeline

• Deploy automatically, but carefully
• Run tests before/during/after deployment and define your benchmarks well!
• Test on past (but relevant) data, performance, system
• Use Canary release technique!
A technique to reduce the risk of introducing a new software version in
production by slowly rolling out the change to a small subset of users before
rolling it out to the entire infrastructure and making it available to everybody.
automatic & safe model updates and deployments
https://martinfowler.com/bliki/CanaryRelease.html

automatic & safe model updates and deployments
• Use Kubernetes
• Use Istio
• Read our guide:
https://cnvrg.io/deploy-models-with-kubernetes/

• Monitor your input data
• Search for unexpected values
• Measure correlation of production data to train data
• Keep it open to dynamically add new tests and rules
• Monitor your predictions
• Model confidence, model bias, and more
• Use Kubernetes, Prometheus and AlertManager: https://prometheus.io/docs/alerting/alertmanager/
monitoring

• Your ML pipeline should be triggered based on
a) Periodically (once a day/week/etc)
b) New training data that is coming in
c) Model decay / model bias / alerts in production
• Make sure to track and validate triggers
trigger retraining

summary
• Continual learning is the future of your ML models
• Continual learning is doable and worth the investment
• Add tests in every stage: data -> train AutoML -> test -> deploy
• Building it on your own? Connect science to engineering
• “Continual Learning in Practice” - By Tom Diethe, Tom Borchert, Eno
Thereska, Borja de Balle Pigem, Neil Lawrence
• Next: Active Learning, Human-in-the-loop, and more

https://cnvrg.io
info@cnvrg.io
+972-506-660186
Talk with an ML specialist

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Cnvrg webinar continual learning

1. Continual Learning WEBINAR How to use continual learning in your ML models Yochay Ettun, CEO yochze@cnvrg.io

2. whoami • Developer/Data scientist => CEO • cnvrg.io – built by data scientists, for data scientists to help teams: • manage, build and automate machine learning • bridge science and engineering

3. agenda • Introduction to Continual Learning • Why do we need continual learning? • An ML Pipeline with Continual Learning • AutoML • Deployment • Monitoring • Live Demo

4. Continual learning (CL) is the ability of a model to learn continually from a stream of data, updating the model in production to maintain performance and relevancy

5. Why Continual Learning? Data is changing

6. Amazon Best Sellers Year 2000

7. Amazon Best Sellers Year 2000 Year 2019

8. December 2017 Bitcoin price 19K

9. December 2017 February 2018 Bitcoin price 19K 6K

10. Year 1599 English Language

11. Year 1599 Year 2016 English Language

12. Best DL Framework Year 2018

13. Best DL Framework Year 2018 Year 2019

14. Year 2017 Top Google Searches

15. Year 2017 Year 2018 Top Google Searches

16. MNIST ML Pipeline with Continual Learning - OCR application to identify numbers from handwritten text - User base is growing, and our model should support new users

17. DATA Data validation AutoML & HPO Model Validations Model Deployment Predictions MNIST ML Pipeline with Continual Learning MonitoringCleaning & Labeling

18. MNIST ML Pipeline with Continual Learning DATA Data validation AutoML & HPO Model Validations Model Deployment PredictionsMonitoringCleaning & Labeling

19. • Data is changing -> Models are changing • Select your algorithm space • For example, for vision: VGG/Inception/ResNet • For each algorithm, specify its range of hyperparameters automl in your continual learning pipeline

20. An idea of AutoML for Computer Vision using Deep Learning

21. And.. All the rest

22. And.. All the rest

23. • Prepare to manage a whole lot more models. • Track everything! • Algorithm, hyperparameters, code version, data version, metrics automl in your continual learning pipeline

24. • ML Ops • Automate your machine learning infrastructure and experimentation using cloud services and Kubernetes! automl in your continual learning pipeline

25. • Deploy automatically, but carefully • Run tests before/during/after deployment and define your benchmarks well! • Test on past (but relevant) data, performance, system • Use Canary release technique! A technique to reduce the risk of introducing a new software version in production by slowly rolling out the change to a small subset of users before rolling it out to the entire infrastructure and making it available to everybody. automatic & safe model updates and deployments https://martinfowler.com/bliki/CanaryRelease.html

26. automatic & safe model updates and deployments • Use Kubernetes • Use Istio • Read our guide: https://cnvrg.io/deploy-models-with-kubernetes/

27. • Monitor your input data • Search for unexpected values • Measure correlation of production data to train data • Keep it open to dynamically add new tests and rules • Monitor your predictions • Model confidence, model bias, and more • Use Kubernetes, Prometheus and AlertManager: https://prometheus.io/docs/alerting/alertmanager/ monitoring

28. • Your ML pipeline should be triggered based on a) Periodically (once a day/week/etc) b) New training data that is coming in c) Model decay / model bias / alerts in production • Make sure to track and validate triggers trigger retraining

29. live example

30. summary • Continual learning is the future of your ML models • Continual learning is doable and worth the investment • Add tests in every stage: data -> train AutoML -> test -> deploy • Building it on your own? Connect science to engineering • “Continual Learning in Practice” - By Tom Diethe, Tom Borchert, Eno Thereska, Borja de Balle Pigem, Neil Lawrence • Next: Active Learning, Human-in-the-loop, and more

31. https://cnvrg.io info@cnvrg.io +972-506-660186 Talk with an ML specialist

Cnvrg webinar continual learning

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