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Scaling Similarity Learning at Digits

Posted Sep 08
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SPEAKER
Hannes Hapke
Hannes Hapke
Hannes Hapke
Machine Learning Engineer @ Digits Financial, Inc.

Hannes was the first ML engineer at Digits, where he built the MLOPs foundation for their ML team. His interest in production machine learning ranges from building ML pipelines to scaling similarity-based ML to process millions of banking transactions daily.

Prior to Digits, Hannes implemented ML solutions for a number of applications, incl. retail, health care, or ERP companies. He co-author two machine learning books:

  • Building Machine Learning Pipeline (O'Reilly)
  • NLP in Action (Manning)
+ Read More

Hannes was the first ML engineer at Digits, where he built the MLOPs foundation for their ML team. His interest in production machine learning ranges from building ML pipelines to scaling similarity-based ML to process millions of banking transactions daily.

Prior to Digits, Hannes implemented ML solutions for a number of applications, incl. retail, health care, or ERP companies. He co-author two machine learning books:

  • Building Machine Learning Pipeline (O'Reilly)
  • NLP in Action (Manning)
+ Read More
SUMMARY

Machine Learning in a product is a double-edged sword. It can make a product more useful but it depends on assumed and strictly defined behavior from users.

Hannes walks through the entirety of their machine learning pipeline, how they implemented it, what the elements are, what the learning looks like, and what tooling looks like.

Hannes maps out what good data hygiene looks like not only from the machine learning perspective down to the software engineering, design, and backend engineering, all the way to the data engineering perspectives.

+ Read More

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