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How Instacart is Using Deep Learning
- 1. How Instacart isUsing Deep Learning to Create the Most Efficient Shoppers Ever
- 2. v @jeremystan Our Value Proposition Groceriesfrom stores you love delivered to your doorstep in as little as an hour + + + =
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- 5. v @jeremystan List Sorting 100s ofretailers 1,000s of stores 10,000s of shoppers 1,000,000s of products
- 6. v @jeremystan Why Not? Can’t wejust? ...build warehouses? No ...get store data? Often not …map manually? Infrequently ...map with tech? Not yet
- 7. v @jeremystan Results 123 years of shopping 125million US households 1% market share 1x trips / week 1 minute saved per trip Shopper Speed (Items per Minute) Batch Size (Items Picked)
- 8. v @jeremystan Problem Definition 1 23 4 5 6 7 8 9 10 Learn sequence Predict sequence
- 9. v @jeremystan Problem Definition Last Candidates Next Prnext= last= ,candidates=
- 10. v @jeremystan Initial Architecture 1 Cross Entropy 1 Deterministic Learned 1m 10m Final Layer 1m Masked SoftMax Hidden Layers 50k 30 10 Embedding 10m 1m 10 tensor length # parameters Predicted 1m = 32% Pr next= last= ,candidates= Embedding 10k 1k 10 10 Embedding 100k 10k
- 11. v @jeremystan 1 10 1m Initial Architecture Embedding Embedding Embedding Hidden Layers Predicted 10m 10k 100k 50k 10m Final Layer 1m 1k 10k 10 1030 10 1m Cross Entropy 1 Deterministic Learned Masked Soft Max 1m tensor length # parameters = 32% Pr next= last= ,candidates=
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- 14. v @jeremystan 1 10 1m Initial Architecture Weaknesses Embedding Embedding Embedding Hidden Layers Predicted 10m 10k 100k 50k10m Final Layer 1m 1k 10k 10 10 30 10 1m Cross Entropy 1 Masked Soft Max 1m Redundant No metadata Dense
- 15. v @jeremystan 1 10 1m Final Architecture Embedding Embedding Embedding Hidden Layers Predicted 10m 10k 100k 50k 10m Final Layer 1m 1k 10k 10 1030 10 1m Cross Entropy 1 Masked Soft Max 1m
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- 17. v @jeremystan 1 10m 10 Final Architecture Shared Embedding Embedding Embedding 10k 100k 50k 1m 1k 10k 10 10 40 1m10 Shared Embedding Hidden Layers Score = 0.7
- 18. v @jeremystan 1 10m 10 Final Architecture Shared Embedding Embedding Embedding 10k 100k 50k 1m 1k 10k 10 10 40 1m10 Shared Embedding Hidden Layers Score = 0.7
- 19. v @jeremystan 1 10m 10 Final Architecture Shared Embedding Embedding Embedding 10k 100k 50k 1m 1k 10k 10 10 40 1m10 Shared Embedding Hidden Layers Score = 0.7 10.16m Score Generator
- 20. v @jeremystan Final Architecture Score 1 10m 10 Shared Embedding Embedding Embedding 10k 100k 50k 1m 1k 10k 10 10 40 1m10 Shared Embedding Hidden Layers = 0.7 10.16m Score Generator
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- 24. v @jeremystan 1m Final Architecture 1 Score Generator Score 1m 1 Score Generator Score 0.7 -0.2 1m1 Score Generator Score 1m 1 Score Generator Score 1m 1 Score Generator Score 10.16m -0.4 1.3 1.1
- 25. v @jeremystan 1m Final Architecture 1 Score Generator Score 1m 1 Score Generator Score 1m1 Score Generator Score 1m 1 Score Generator Score 1m 1 Score Generator Score 1 Predicted Cross Entropy 1 Soft Max 5 = 36% 10.16m 10x faster training 10% better accuracy 2x slower prediction
- 26. v @jeremystan Team Francisco Guzman Designer Nima Zahedi ProductManager Montana Low Machine Learning Engineer
- 27. WE’RE HIRING! @jeremystan |e-mail: jeremy.stanley@instacart.com careers: careers.instacart.com