Machine learning projects often fail to make it from development to production. Looking at the full machine learning lifecycle is essential for success. The lifecycle includes development, deployment, infrastructure, monitoring, automation, standardization, lineage and reproducibility. A machine learning operations (MLOps) platform can provide an end-to-end system view for increased efficiency, collaboration, and trust across the lifecycle. Key takeaways are to focus on what is important, avoid doing nothing which fails to scale, and doing everything which stifles progress.

2. Challenges faced with
machine learning in practice
12:15 Introduction
12:20 Machine learning lifecycle
12:30 Different aspects
12:50 Cubonacci
12:55 Key takeaways

3. Introduction
Jan van der Vegt
Data science background
Founded Cubonacci in 2018
MLOps platform
https://www.linkedin.com/in/jan-van-der-vegt/

4. 80% of projects intended for production don’t make it
Why?

5. Machine learning lifecycle

6. Machine learning lifecycle

7. Infra
Versions
SecurityLive data
Monitoring
Machine learning lifecycle

8. Hidden technical debt in machine learning systems
https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf

9. Machine learning lifecycle

10. 80% of projects intended for production don’t make it
Looking at the full machine learning lifecycle, from
development to maintenance is essential for success
Machine Learning Operations

11. MLOps goals
Efficiency
Value faster
Collaboration
DRY
Trust
Increased usage
Peace of mind
Process

12. Aspects
Development
Deployment
Infrastructure
Monitoring
Automation
Standardization
Lineage
Reproducibility
Overview

13. Development
Iterative
Fast
Messy
Rigid
Slow
Reliable

14. Deployment
Types:
Request based
Scheduled batch
Streaming
Important:
Frequent
Fast
Autonomous

15. Infrastructure
Dynamic Resource
based

16. Monitoring
Feedback
Model metrics
Data quality
Input data
Predictions
Availability
System on?
Requests
Alerts
Severity
Responsibilities
Diagnostics
Logs
Inputs

17. Automation
Iteration Fewer mistakes Change

18. Standardization
Automation Collaboration Flexibility

19. Lineage
Source data
Code
Data snapshot
Experiment
Model Prediction
New data
Feedback
Monitoring

20. Prediction
Reproducibility
ExperimentData
Reconstructable
Save snapshot itself
Random number generators
Hyperparameters
Input data
Trust
Diagnosis
Regulations

21. Overview
End-to-end system
Unified view on the system
Communication significantly easier
Trace back any issues to the source

22. Cubonacci
Flexibility
Design Efficiency

23. Takeaways
- Keep the full machine learning lifecycle in mind
- Figure out what is important
- Doing nothing fails to scale
- Doing everything stifles progress

24. www.cubonacci.com +31 (0) 20 308 43 90 info@cubonacci.com