The document discusses the importance of reproducibility in machine learning for manufacturers, emphasizing that reproducibility requires the same environment, raw data, and unmodified code. It highlights the benefits of reproducibility, such as stable results and easier operationalization, alongside recommendations for ensuring data integrity and accessibility. The document also mentions frameworks like MLflow and Azure ML to facilitate reproducible practices.

1. Helping manufacturers innovate through AI
Reproducible machine learning
Steph Locke
@theStephLocke

2. Steph Locke
CEO @ Nightingale HQ
Data & AI specialist
Microsoft MVP, 5+ years
Microsoft MCT
T: @theStephLocke
Li: /stephanielocke
steph@nightingalehq.ai

3. Agenda

4. Reproducibility

5. Reproducibility
Data
Same Different
Analysis
Same Reproducible Replicable
Different Robust Generalisable

6. It’s reproducible if…
With the same ✨ environment
With the raw ✨ data
With the unmodified ✨ code
➔Produces exactly the same results

7. Benefits

8. You/Team
Fewer headaches around environments and data
Less rework of code
Clear standards
Easier operationalisation

9. Stakeholders
• Stable results
• Auditable
• Maintainable / correctable
• Easier operationalisation

10. Recommendations

11. Findable
- Unique name / ID
- Documented
Accessible
- Common access methods
- Open protocol
- Metadata stored even after data may
be removed
FAIR data
Interoperable
- Common standards
- Terms defined
Reusable
- License and use rights specified
- Provenance documented

12. Use
• Logging
• Version control
• Fixed seeds*
• Dependency tracking

13. Framework examples
• MLFlow
• {drake}
• Azure ML
• FairML

14. A trusted marketplace for
digital and AI products
and services for
manufacturers.
NightingaleHQ.ai
nightingale-hq
nightingalehqai
nightingalehq
Helping manufacturers
innovate through AI
14
Thanks!
• @theStephLocke
• steph@nightingalehq.ai