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Clipping · Exhibit · Large-Scale Data Mining Pipeline

Pasted from the desk

Distributed Lead Scorer

A distributed data mining pipeline using PySpark and PyTorch DDP that processes 100M events/day and predicts conversion probability in real-time using a Deep Interest Network.

Halftone photograph: a mail-sorting hall, pigeonholes stretching into the distance.
Distributed Lead Scorer — the sorting hall, file photo.
122a34567Fig.1.Fig.2.a pigeonhole, sectioned

Reference

  1. 1.HOPPER — 100M+ events a day
  2. 2.PIGEONHOLES — PySpark — the sort
  3. 3.BELT — the hall†
  4. 4.MILLWHEEL — PyTorch DDP — Deep Interest Network
  5. 5.STAMP — checkpointing — zero loss
  6. 6.GAUGE — conversion†
  7. 7.LEDGER — the run log†

† composed from the archives

APPARATUS FOR A SORTING HALL. Filed May 2025.

122a34567Fig.1.Fig.2.a pigeonhole, sectioned

Tech

  • PySpark
  • PyTorch DDP
  • Deep Interest Network
  • Distributed Computing

The line

  1. 01

    Engineered a distributed data mining pipeline using PySpark to process and feature-engineer 100M events/day, reducing data latency from hours to minutes.

  2. 02

    Developed and deployed a parallelized Deep Interest Network (DIN) using PyTorch DDP across a multi-GPU cluster for real-time conversion prediction.

  3. 03

    Built an automated model evaluation framework for continuous performance monitoring.

  4. 04

    Implemented a robust fault-tolerance strategy using checkpointing, ensuring zero data loss during multi-hour distributed training runs.

Measurable impact

100M events/day processed

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