Abstract: "Are you interested in the practical challenges the brave new world of machine learning has for the DevOps professionals? Have you wondered how one tests machine learning applications? In short, have you ever been pained by how revolutionary tech stacks have made regular DevOps practices irrelevant or costly? If yes, this session is for you. When it comes to Machine Learning applications, we need to rethink DevOps a little bit. What to test when the output of the system is not deterministic? How does one know optimal training is done and quality is acceptable? In such tech stacks, deterministic DevOps is impossible. This talk aims to explore the challenges DevOps practitioners face at the face of revolutionary tech stacks and some meaningful ways of dealing with such challenges. Come join us to explore how one could practice DevOps in machine learning apps with examples and demos." Key Takeaways: 1.A high-level understanding of Machine Learning as a field 2.Understanding of how machine learnings apps are different from traditional apps 3.Overview of tools involved in machine learning 4. Understanding of how to implement DevOps in Machine Learning apps 5. Inspiration to try out few things (Free Source Code).

1. Reinventing DevOps for
Machine Learning Apps
Vivek Ganesan
AGILE GURUGRAM

2. Takeaways
Intro to DevOps and Machine Learning
Demo of sample ML application
Changes in DevOps for ML applications - when
compared to traditional applications
Pipelines for ML applications
Source code used for demos

3. Agenda
• Introduction to DevOps
• Machine Learning - A Layman’s Intro
• Demo - A Sample Machine Learning App
• Activity: Fundamental Changes
• Demo - How do we reinvent DevOps for ML?
• Summary
• Q&A

4. What is DevOps?

5. How do Human Beings Learn?

9. How do Machines Learn?
Coding ValidationModeling Training
Deployme
nt

10. How do Machines Learn?
Coding ValidationModeling Training
Deployme
nt

11. Demo - Sample
Machine Learning App

12. Activity: Fundamental
Changes

13. Fundamental Changes
assertThat(computedSum).isEqualTo(10);
assertThat(correctPredictionPercentage).is
GreaterThanOrEqualTo(80);

14. Fundamental Changes
Monitoring runtime related metrics
Monitoring user feedbacks also

15. Fundamental Changes
Optimizing for zero downtime code
deployment
Optimizing for zero downtime training also

16. Fundamental Changes
Continuous Builds &Tests
Continuous accuracy checks also

17. Demo: Reinventing
DevOps for ML

18. Questions???
All code used in this talk can be found at
https://github.com/vivganes/devops-machine-learning-example