What MLOps is and why it is crucial for the efficiency of your ML developments.

1. ML-Ops
Philosophy, Best-Practices and Tools
Jorge Davila-Chacon, PhD
Co-founder and CTO
Heldenkombinat Technologies GmbH
HELDENKOMBINAT
MACHINE LEARNING
ARIC Brown-Bag Sessions
28.04.2020

2. 5 Dimensions of ML Projects
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Algorithmic
Efficiency
Hardware
Infrastructure
Data
Requirements
Strategy &
Regulations
ML Design
Patterns
Real-World
Prototype

3. 5 Dimensions of ML Projects
HELDENKOMBINAT
MACHINE LEARNING
2020 © Heldenkombinat Technologies GmbH
Algorithmic
Efficiency
Hardware
Infrastructure
Data
Requirements
Strategy &
Regulations
ML Design
Patterns
Real-World
Prototype

4. Philosophy

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Philosophy
“DevOps is an organizational and cultural movement that aims to
increase software delivery velocity,
improve service reliability,
and build shared ownership among software stakeholders.”
- Google [1]
“At its simplest,
DevOps is about removing the barriers between two traditionally siloed teams,
development and operations.”
- AWS [2]
[1] https://cloud.google.com/devops
[2] https://aws.amazon.com/devops/what-is-devops/

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Philosophy
“the extension of the DevOps methodology to include Machine Learning and Data
Science assets as first class citizens within the DevOps ecology”
- Continuous Delivery Foundation’s SIG-MLOps [1]
“a software engineering approach in which a cross-functional team produces
machine learning applications based on code, data, and models in small and safe
increments that can be reproduced and reliably released at any time,
in short adaptation cycles.”
- Martin Fowler [2]
[1] https://github.com/cdfoundation/sig-mlops
[2] https://martinfowler.com/articles/cd4ml.html

7. Best Practices

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What’s sticking?
• Continuous Integration
• Continuous Delivery
• Microservices
• Infrastructure as Code
• Communication and Collaboration

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What’s new?
From:
• Code versioning
To:
• Code versioning
• Model versioning
• Data versioning

10. Tools

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Platforms
Data/Model versioning:
• https://dvc.org
• https://github.com/pachyderm/pachyderm
• https://delta.io
• https://hudi.apache.org
• https://datahub.io
Workflows/Experiment tracking:
• https://www.floydhub.com
• https://www.wandb.com
• https://www.paperspace.com
• https://www.tensorflow.org/tfx
• https://aws.amazon.com/sagemaker

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Literature

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Literature
MLOps with a Feature Store
https://www.logicalclocks.com/blog/mlops-with-a-feature-store
Hidden Technical Debt in Machine Learning Systems
https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf
Studying Software Engineering Patterns for Designing Machine Learning Systems
https://arxiv.org/pdf/1910.04736.pdf
Machine Learning Architecture and Design Patterns
http://www.washi.cs.waseda.ac.jp/wp-content/uploads/2019/12/IEEE_Software_19__ML_Patterns.pdf
Patterns (and Anti-Patterns) for Developing Machine Learning Systems
https://www.usenix.org/legacy/events/sysml08/tech/rios_talk.pdf
Patterns for machine learning-powered features
https://material.io/collections/machine-learning/patterns-for-machine-learning-powered-features.html

14. Take away

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Wrap Up
What are MLOps?
Principles for automating the:
• Building
• Testing
• Deployment
• Maintenance
of ML systems.
Why are MLOps crucial?
Is a proven approach to improve:
• Speed
• Delivery
• Reliability
• Scaling
• Collaboration
• Security
in ML developments.

16. Let’s connect in LinkedIn!
We deliver advice, skills and business opportunities in all machine learning playing fields.
Jorge Davila-Chacon, PhD
Co-founder and CTO
Focus
AI | Reinforcement Learning
Data Science
Robotics
jorge @ heldenkombinat.com
HELDENKOMBINAT
MACHINE LEARNING
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