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Adopting software design practices for better machine learning

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Jeff McGehee, Senior Data Scientist and IoT Practice Lead, Very

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Adopting software design practices for better machine learning

  1. 1. Jeff McGehee Data Scientist, IoT Practice Lead The Process is Everything An interpretation of Google’s Rules of Machine Learning (https://goo.gl/9AKRvC)
  2. 2. The Theory
  3. 3. It’s easy to optimize your model loss, but it’s hard to optimize value delivered. 3
  4. 4. Introducing SoDQoP 4 Speed of Delivery ● Time to market ● Avg speed of new features (agility) Quality of Product ● User Experience ○ Reliability ○ Availability ○ Ease of use ○ Valuable Features
  5. 5. SoDQoP Over Time 5 100SoDQoP Time
  6. 6. SoDQoP Over Time (cont’d) 6 100 TIME SoDQoP
  7. 7. 7 Aggregate Team SoDQoP People Tooling Process GPU Tensor Processing Research First Losers Winners Google’s Rules of ML
  8. 8. Process is the place where machine learning has the most room for improvement.
  9. 9. 9 Process
  10. 10. Scientists make discoveries. Engineers develop predictable processes for aggregating and leveraging these discoveries in the world at large. From (Computer) Science to (Software) Engineering 10
  11. 11. Build lean. Be clever with machine learning APIs. Make it easy to improve your model. Machine Learning Engineering 11
  12. 12. Build Lean 12 In General ● Understand what you’re measuring, and why. ● Fail fast. ● Take “ship early and ship often” seriously. Don’t waste time chasing a few percentage points of accuracy on a feature that users haven’t even been exposed to yet. Things We Do ● Two weeks or less to validate ML as a viable solution.* ○ Jupyter, Pandas, SKLearn, TF, Keras, Matplotlib/Seaborn. ● If you haven’t failed, ship what you have. ○ Serverless framework (AWS Lambda, S3, Sagemaker, Batch). ● Iterate (rapidly) as needed.* *Should be led by “Understand what you’re measuring, and why”
  13. 13. Be clever with ML APIs 13 In General ● Don’t reinvent the wheel. ● Have a deep understanding of accuracy requirements. ● Build custom solutions where they will have the highest impact. Things We Do ● Wrap “noisy” API models with a Bayesian inference engine tuned to improve desired accuracy metric. ● Obtain features from API models (object detection), and pass those to a final model.
  14. 14. Make it easy to improve your model(s) 14 In General ● Record your predictions, along with ground truth (if possible). ● Build features that allow users to label or make corrections on predictions. ● Collect data that might be leveraged for future models. Things We Do ● AWS Lambda endpoints for receiving data related to model feedback. ● High test coverage to facilitate agility around changing the model.
  15. 15. Questions?

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