Mat Tuli's presentation at the April 29th Meetup.

1. Challenges of Machine Learning in Production
Mathieu Tuli

2. ● Deployment of research
● Unstructured vs. structured data
● Custom solutions
● Working with clients
What Does it Mean?

3. ● The importance of engineering
● Engineering vs. research mindset
● There are exceptions
Engineering

4. ● Acceptable error
● Coding style and practices
● The lego model
● Curated datasets
Production vs. Academic Research

5. Curated Dataset

6. Curated Dataset
99%
84%
64%
57%

7. Curated Dataset
100%
96%
98%
100%
94%
93%

8. ● Tangible results
● Optimization
Production vs. Academic Research

9. ● Distillation
● Quantization and weight sharing
● Weight Encoding (data compression)
● Pruning
Optimization

10. Model Size Reduction

11. ● Researchers don’t code for deployment
● The lego model isn’t perfect
● Building your own models
● Hardware
● Data privacy
Bottlenecks

12. ● On-premise deployment
● Client requirements and knowledge
● Adversarial examples
Problems

13. Adversarial Examples

14. ● Vanilla computer vision vs. machine learning
● Chatbot vs. frequently asked questions (FAQ)
AI vs. IA

15. ● Format your data (consult with experts on
how)
● Being first isn’t everything
● Know what you want (start small)
● Work with a trustworthy partner
As A Company...

16. ● This is not the full picture
● Do not underestimate the importance of
engineering
● But, research is foundational
● IA vs. AI
Concluding Remarks

17. Thank You