Christoph Schranz
Guest Lecturer at Salzburg Research
Salzburg, Salzburg, Austria
Summary
👤
Senior🎓
Top SchoolChristoph Schranz is a Data Scientist and DevOps-savvy engineer with a decade of experience building reproducible ML and geospatial workflows from Salzburg. At Salzburg Research he evolved from data engineer to data scientist, deploying production-ready pipelines and GPU-accelerated Jupyter environments for TensorFlow and PyTorch that streamline collaborative deep learning experiments. As a guest lecturer at UNIGIS Salzburg he teaches applied statistics, machine learning and deep learning in geoinformatics, bridging academic rigor with practical deployment know-how. His background in electrical engineering and education (MSc Data Science, BEd Mathematics & Physics) gives him a rare combination of systems thinking and didactic clarity. Active on GitHub and ResearchGate, he focuses on automation, container orchestration and reproducibility—often improving developer workflows rather than just model code.
10 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Data Science, 1.3, Master of Science - MS, Data Science, 1.3 at Paris Lodron Universität SalzburgIngenieur, Elektrotechnik, 1.5, Ingenieur, Elektrotechnik, 1.5 at HTBLuVA Salzburg
Stackoverflow
815reputation
635kreached
23answers
0questions
Github Skills (20)
environmental10
docker10
bash10
docker-compose10
dockers10
cicd10
enviroment10
ubuntu10
environ10
swarm10
gpu-acceleration9
jupyterlab8
sorting6
gpu6
groupby6
Programming languages (8)
Github contributions (5)
iot-salzburg/gpu-jupyter
Nov 2019 - Nov 2022
GPU-Jupyter: Leverage the flexibility of Jupyterlab through the power of your NVIDIA GPU to run your code from Tensorflow and Pytorch in collaborative notebooks on the GPU.
Role in this project:
DevOps Engineer
DevOps EngineerContributions:9 releases, 193 commits, 78 PRs in 3 years
Contributions summary:Christoph primarily focused on automating the deployment process and configuring the environment for the GPU-Jupyter instance. They created and modified scripts for adding the project to a Docker Swarm, parameterizing network configurations and registry ports. Further commits automated the Dockerfile generation process and container builds, while also setting up a simplified workflow for local deployments. The user also updated the project to the jupyterlab 3.x version.
pytorchnvidia-dockernvidiagpujupyter-notebook
iot-salzburg/PantaRhei_nimble
Jul 2019 - May 2022
integrate PantaRhei data to a database as it could have been negotiated through the nimble platform.
Contributions:1 PR, 4 pushes, 1 branch in 2 years 10 months
integratesqldatabasenimble
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