Hannes Hapke
Open Source at Google Developer Experts
Portland, Oregon, United States
Summary
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SeniorHannes Hapke is a seasoned machine learning engineer and author based in Portland, Oregon, with over a decade of experience delivering production-ready ML and NLP solutions. As a Principal Machine Learning Engineer at Digits since 2020, he leads scalable pipelines and model deployment across teams, drawing on a track record that spans SAP Concur, Cambia Health Solutions, and startup leadership. An active community builder, he co-authored O'Reilly's Building Machine Learning Pipelines and NLP in Action, and serves as a Google Developer Expert in Machine Learning and a Google Developer Advisory Board Member. His open-source contributions span Django back-end utilities, ML pipelines, and TensorFlow/TFX-based workflows, reflecting hands-on work from data ingestion to deployment. He has led cross-disciplinary teams and advised on AI strategy while maintaining hands-on coding with Python, Keras, and TensorFlow. In addition to corporate roles, he has led and advised startups like Wunderbar.ai and co-founded renooble, underpinning a blend of technical depth and entrepreneurial drive.
12 years of coding experience
10 years of employment as a software developer
Github Skills (25)
unit-testing10
multi-view10
data-pipelines10
python10
django10
tfx10
machine-learning10
data-preprocessing10
keras10
page-views10
tensorflow210
tensorflow10
bert10
pipe10
pipeline10
Programming languages (11)
Github contributions (5)
Building-ML-Pipelines/building-machine-learning-pipelines
Jan 2020 - Nov 2021
Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson
Role in this project:
Data Scientist
Data ScientistContributions:2 releases, 3 reviews, 110 commits in 1 year 10 months
Contributions summary:Hannes contributed to the development of a machine learning pipeline, as evidenced by the addition of utility scripts for data splitting, and the creation of a Keras-based model experiment notebook. The primary focus of the work appears to be around data ingestion, preprocessing and model building. The user also focused on visualizing the model.
machine-buildingdata-sciencehannesmlmachine-learning
tensorflow/workshops
Mar 2020 - Nov 2020
A few exercises for use at events.
Role in this project:ML Engineer
ML EngineerContributions:19 commits, 5 PRs, 10 comments in 8 months
Contributions summary:Hannes primarily contributed to a TFX pipeline designed for processing and training sentiment analysis models using BERT. Their work involved updating the notebook to include the setup of an ALBERT model and integrating it with the existing pipeline. The user also focused on correcting and refining the model architecture and data preparation steps, including handling the input data structure for the ALBERT model. Furthermore, they adjusted pipeline configurations and package installations to ensure compatibility with the updated TF and TFX versions.
javaevents
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