This webinar will instruct data scientists and machine learning engineers how to build manage and deploy auto-adaptive machine learning models in production. Data is ever changing, leaving your models outdated and built on old data. This can lead to underperforming models and a lot of manual work to fix it. By allowing your models to continually learn you’ll ensure that they run at peak performance. Using state of the art Kubernetes infrastructure, we’ll show you how to automatically track and manage your auto-adaptive machine learning models while in production. By building auto-adaptive machine learning models, data engineers can bridge the gap between research and production. After this webinar you’ll be able to build and deploy machine learning pipelines that automatically adapt and retrain based on any validation trigger you choose. Key webinar takeaways: How to build auto-adaptive machine learning pipelines How to use Kubernetes to manage and scale models in production How to automatically monitor for peak performance How to set up continuous deployment of ML pipeline Watch all our webinars at https://cnvrg.io/webinars-and-workshops/

1. Auto-adaptive machine learning
How to use continual learning for your ML models
Yochay Ettun, CEO
@yochze
yochze@cnvrg.io

2. whoami
• Developer/Data scientist => CEO
• cnvrg.io = built by data scientists, for data scientists to help teams:
• Get from data to models to production in the most efficient and fast way
• bridge science and engineering

3. agenda
• Introduction to continual learning
• Why do we need continual learning
• ML pipeline with continual learning
• AutoML
• Deployment
• Monitoring
• Summary

4. Introduction to Continual Learning
• Unified analytics engine for large-scale data processing
• Faster processing speed of applications due to In-memory cluster computing (100x
improvement)
• Support different workloads – batch, iterative, streaming, interactive SQL etc.
• Support multiple languages and different environments

5. ML pipeline with Continual Learning

6. Why do we need continual learning?

7. Why do we need continual learning?

8. ML pipeline with Continual Learning

9. AutoML and Hyperparamaters Optimization
• Data is changing à Models are changing
• Train your model on different algorithms space:
• Tune each algorithm with hyperparameters
• Choose best model performance result

10. AutoML and Hyperparamaters Optimization
• Train Models at scale using kubernetes
• Track your models results
• Compare between them

11. Automatic & safe model deployments
• Deploy new version automatically but safely
• Run tests during updating your production models
• Use Canary deployment technique:
• Reduce the risk of deployment
• Rolling updates slowly using validation in each step

12. Automatic & safe model deployments
• Use Kubernetes
• Use Istio
• Read our guide

13. Monitoring your models
• Use Prometheus, kubernetes & alert manager
• Monitor input data:
• Search for unexpected data
• Measure the correlation of production data to train data
• Monitor prediction:
• Model confidence, model bias, and more
• Add validations on the fly

14. Monitoring your models

15. Trigger retraining
• Trigger retrain based on:
• Periodically (Once in a day/week/month)
• New training data that comes in
• model decay / model bias / alerts in production
• Make sure to track and monitor alerts

16. Summary
https://d39w7f4ix9f5s9.cloudfront.net/cf/6d/f484e8e5445cba
91a63eddd2b1c8/continual-learning-in-practice.pdf

17. Thanks!
https://cnvrg.io
info@cnvrg.io
+972-506-660186