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Scaling Similarity Learning at Digits

Posted Sep 08, 2022 | Views 1K
# Digits Financial, Inc.
# Digits
# Digits.com
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SPEAKERS
Hannes Hapke
Hannes Hapke
Hannes Hapke
Principal Machine Learning Engineer @ Digits

As the principal machine learning engineer at Digits since 2020, Hannes Hapke is fully immersed in day-to-day, evolving innovative ways to use machine learning to boost productivity for accountants and business owners. Prior to joining Digits, Hannes solved machine learning infrastructure problems in various industries including healthcare, retail, recruiting, and renewable energies.

Hannes is an active contributor to TensorFlow’s TFX Addons project and has co-authored multiple machine learning publications including the book "Building Machine Learning Pipelines" by O’Reilly Media. He has also presented state-of-the-art ML work at conferences like ODSC, or O’Reilly’s TensorFlow World.

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As the principal machine learning engineer at Digits since 2020, Hannes Hapke is fully immersed in day-to-day, evolving innovative ways to use machine learning to boost productivity for accountants and business owners. Prior to joining Digits, Hannes solved machine learning infrastructure problems in various industries including healthcare, retail, recruiting, and renewable energies.

Hannes is an active contributor to TensorFlow’s TFX Addons project and has co-authored multiple machine learning publications including the book "Building Machine Learning Pipelines" by O’Reilly Media. He has also presented state-of-the-art ML work at conferences like ODSC, or O’Reilly’s TensorFlow World.

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Demetrios Brinkmann
Demetrios Brinkmann
Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Vishnu Rachakonda
Vishnu Rachakonda
Vishnu Rachakonda
Data Scientist @ Firsthand

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.

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Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.

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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.

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