Ottawa Machine Learning Meetup April 24, 2017 Slides. Abstract: Amazon is one of the world's fastest growing companies and is showing no signs of slowing down. This staggering growth often presents serious challenges to the company's ability to scale. In the past, this has tested (and occasionally crushed) conventional software systems, but now this is testing the human decision makers in areas from Product Recommendation to Produce Inspection, from Text Classification to Voice Assistants and more. Matthew Spencer, a Machine Learning Engineer at Amazon will discuss these challenges and how they are being met by a wide variety of Machine Learning applications.

1. Machine Learning @

2. Matthew Spencer, PhD
Ottawa, Canada
◦ BASc Computer Engineering from University of Ottawa
◦ Software Development in Ottawa (Thales, etc)
Reading, UK
◦ PhD Cybernetics (Neural Interfaces)
◦ Post Doc
◦ Cognition as communication
◦ Brain Embodiment Lab
And back to Ottawa/Seattle
◦ ML-SDE at Amazon - Seller Experience
◦ Case modelling and classification

3. Academic vs Industry
One and done vs Continuous deployment
Clever new ideas vs Bullet-proof systems
Understanding vs Doing
Complexity vs Simplicity

4. Humans should make
decisions that matter
Machine learning can handle routine decisions, leaving to humans only the ones that matter
◦ Full automation
◦ Produce evaluation
◦ Categorization
◦ Translation*
◦ Human in the loop
◦ Recommendations
◦ Forecasting

5. Scaling Routine Decisions
Doing the same thing a lot more
Expertise is limited
Time is limited
Patience is limited?
Examples
◦ Platforms
◦ AWS (AML, Lex)
◦ Hyper-parameter Optimization
◦ Hosting and automation pipelines
◦ Translation
◦ Product pages
◦ Global cases
◦ Forecasting
◦ Supplies of 500k+ products
◦ Demand on 5000+ Associates

6. Routine Insight
Finding wisdom in a sea of data…
… but maybe we’ll settle for knowledge
Examples
◦ Summaries
◦ Products
◦ Reviews
◦ Recommendations
◦ Tools
◦ Products
◦ Procedures

7. Standardizing Routine Decisions
Doing the same thing every time
Different experience
Different perspective
Not interested in opinion
Makes results more applicable
Examples
◦ Categorization
◦ Products
◦ Problems
◦ Quality control
◦ Produce
◦ Organization

8. ML Problems at Amazon
EXAMPLES

9. Demand
Forecasting
◦ Softlines
◦ Call centres

10. Image processing
◦ Amazon Fresh produce monitoring
◦ Amazon Go facial recognition

11. Recommendations
◦ Entity embeddings – products,
cases, etc
◦ Prime Video recommendations
◦ Personalization

12. Natural language
processing
◦ Alexa/Lex – speech to text, text to speech
◦ Text classification
◦ Review summaries
◦ Review sentiments
◦ Kindle summaries
◦ Machine translation – ASIN, AGS

13. Scaling
◦ Experimentation
◦ Retraining
◦ Hosting
◦ Optimization

14. Drones

15. Thank you!
QUESTIONS?