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What is
MLOps?
V 0.1
What ML development should look like
Design
10% Model
Development
90% Glue
Coding
Complex &
unstructured
handover
Operations
Time to Market
What it actually looks like
Why?
ML Code
Needed to
prove value
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
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.
MLOps is not a
single tool or
platform. Tooling
MLOps is about
agreeing to do ML
the right way and
then supporting it.
Process
Tooling
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.
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
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.
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
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
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
MLOps should fix the development loop.
Process
Tooling
Check out more reading materials
at valohai.com/mlops

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What is MLOps

  • 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
  • 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