MLOps aims to streamline the deployment of machine learning models from idea to production with minimal risk and friction. It emphasizes collaborative, reproducible, and continuous processes while requiring various tools for data analysis, model serving, and monitoring. The document outlines the importance of shared principles and considerations in implementing MLOps effectively.

1. What is
MLOps?
V 0.1

2. What ML development should look like

3. Design
10% Model
Development
90% Glue
Coding
Complex &
unstructured
handover
Operations
Time to Market
What it actually looks like

4. Why?
ML Code
Needed to
prove value

5. Why?
Because most of your ML system is
not about the ML code.
Data Verification
Serving
Infrastructure
Configuration Data Collection
Feature Extraction
Process Management Tools
Analysis Tools
Monitoring
Machine Resource
Management
ML Code
Hidden Technical Debt in Machine Learning Systems (2015): https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf
Needed to
materialize
value

6. The goal of MLOps is to reduce technical friction
to get the model from an idea into production in
the shortest possible time with as little risk as
possible.

7. MLOps is not a
single tool or
platform. Tooling

8. MLOps is about
agreeing to do ML
the right way and
then supporting it.
Process
Tooling

9. Process
A few shared principles will take you a long way…
ML should be
collaborative.
ML should be
reproducible.
ML should be
continuous.
ML should be tested
& monitored.

10. Tooling
And tooling will help implement your process.
ML should be
collaborative.
ML should be
reproducible.
ML should be
continuous.
ML should be tested
& monitored.
Shared
Infrastructure
Versioning for Code,
Data and Metadata
Machine Learning
Pipelines
Model Deployment
and Monitoring

11. Time Risk
Short-term thinking
Long-term thinking
Do it fast Do it safe
Time
Do it right So it’ll be faster
next time
Risk
Avoid the “It’s too early for MLOps” trap.

12. Data analysis
ML pipeline Model serving
Model monitoring
Experimentation
Feature store
Metadata store
Model registry
Code repository
The MLOps Stack
provides one way to
think about what
tooling you might
need.
Download template:
valohai.com/mlops

13. Data analysis
ML pipeline Model serving
Model monitoring
Experimentation
Feature store
Metadata store
Model registry
Code repository
Considerations:
Self-Managed Tools
vs
Managed Tools
Point Solutions
vs
End-to-End Platforms
Download template:
valohai.com/mlops

14. The MLOps tools are aplenty…
but so are the requirements.
Self-driving car vs
recommendation
engine
Citizen data scientists
vs seasoned engineers
Healthcare & fintech vs
mobile gaming
Bootstrapping vs
well-funded
Use Case Team & skills Regulation Other resources

15. MLOps should fix the development loop.
Process
Tooling

16. Check out more reading materials
at valohai.com/mlops