Machine Learning Engineer | Interactive Data Visualization

3/2020 – 6/2020

Map Matching of vehicle position data
Input: Billions of noisy vehicle telemetry data points (position, direction etc.)
Output: Vehicle trajectories matched to street
The algorithm is scheduled periodically on AWS Batch to process historic telemetry data stored on S3. I used GraphHopper Map Matching and implemented a Particle Filter that leads to matched trajectories of high quality. The quality of the processed trajectories is measured and can be monitored using AWS CloudWatch.
Technologies
AWS Batch, Lambda, S3, CloudWatch, Docker, Python, Pandas, OSM, Graphhopper

Pandas, Docker, Git, Python, Amazon Web Services (AWS)

12/2019 – 2/2020

Frontend and Backend for managing models / clusters for a Fleet ML service
A UI (and Backend for the Frontend) was built to make an ML solution easy to expand to new cities and easy maintanable. The UI makes it possible to manage and extend the services for people that are not deeply involved into the technical details. A specialty about the project is the storage of the meta data (cities, clusters, POIs) via GitHub API to allow the 4-eyes principle via GitHub PRs and enable versioning and rollbacks.
Technologies
AWS ECS, S3, Docker, GitHub API, Python, ol3js, Vue.js, tailwindcss

Docker, Git, Python, Amazon Web Services (AWS), JavaScript, Vue.Js

9/2019 – 11/2019

Interactive Vehicle ETA prediction visualization.
For a shuttle service 3 different estimations of arrival time (ETA) are used. To track their performance over time and by the given circumstances these ETAs were collected and compared by their actual arrival time. An interactive visualization (Backend & Frontend) was created to analyze the different ETAs.
Technologies:
data collection: AWS Lambda, S3
backend: AWS ECS, Docker, Python Flask
frontend: Vue.js, ol3js, d3.js

Docker, Git, Python, Amazon Web Services (AWS), JavaScript, Vue.Js