End-to-end MLOps integration: a case study with ISS telemetry data
Publication date
2024-03
Document type
Conference paper
Author
Geier, Christian
Creutzenberg, Martin
Pfeifer, Jann
Turk, Samo
Organisational unit
Publisher
UB HSU
Book title
Machine learning for cyber physical systems
Part of the university bibliography
✅
Language
English
Keyword
MLOps
Kubeflow
ISS
Anomaly detection
Abstract
Kubeflow integrates a suite of powerful tools for Machine Learning (ML) software development and deployment, typically showcased independently. In this study, we integrate these tools within an end- to-end workflow, a perspective not extensively explored previously. Our case study on anomaly detection using telemetry data from the International Space Station (ISS) investigates the integration of various tools—Dask, Katib, PyTorch Operator, and KServe—into a single Kubeflow Pipelines (KFP) workflow. This investigation reveals both the strengths and limitations of such integration in a real-world context. The insights gained from our study provide a comprehensive blueprint for practitioners and contribute valuable feedback for the open source community developing Kubeflow.
Version
Published version
Access right on openHSU
Open access