SwiggyFood Delivery & Quick Commerce5M daily orders · 500K restaurants · 1M delivery partners · 50M monthly active users
Key People & Companies
MIMO Deep Learning Model
Swiggy · Internal
Swiggy
Entity Embeddings
Swiggy · Framework
ETA Model
Swiggy · Internal
Soumyajyoti Banerjee
Data Scientist at Swiggy
TensorFlow
Google · Framework
DSP (Data Science Platform)
Swiggy · Platform
Sunil Rathee
Swiggy
+ 8 more entities in the full study
Key Results
- Training time reduced to one-fifth of previous approaches, with training memory footprint cut by approximately 50%. This allowed models to train in-memory rather than requiring disk-based operations, according to Swiggy's engineering blog.
- Order-to-assignment (O2A) prediction accuracy improved by nearly 30%, helping the assignment system allocate delivery executives faster and more efficiently. As the team noted, 'This helped our exception management systems to become more efficient at recovering from delays in DE assignment.'
- Overall delivery time prediction (O2R) mean absolute error decreased by 5% after introducing in-model embeddings, improving customer experience through more accurate delivery promises.
- + 4 more results inside
“Having separate networks for each of the outputs was not only inefficient to train and deploy but also left unrealised performance benefits on the table.”
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