Category: System Engineering
Reproducibility in Machine Learning
The rise of Machine Learning has led to changes across all areas of computer science. From a very abstract point of view, heuristics are replaced by black-box machine-learning algorithms providing “better results”. But how do we actually quantify better results? ML-based solutions tend to focus more on absolute performance improvements (measured by metrics) instead of…
Experiences from breaking down a monolith (3)
Written by Verena Barth, Marcel Heisler, Florian Rupp, & Tim Tenckhoff DevOps Code Sharing Building multiple services hold in separated code repositories, we headed the problem of code duplication. Multiple times a piece of code is used twice, for example data models. As the services grow larger, just copying is no option. This makes it…
Migrating to Kubernetes Part 1 – Introduction
Written by: Pirmin Gersbacher, Can Kattwinkel, Mario Sallat
Migrating to Kubernetes Part 2 – Deploy with kubectl
Written by: Pirmin Gersbacher, Can Kattwinkel, Mario Sallat
Migrating to Kubernetes Part 3 – Creating Environments with Helm
Written by: Pirmin Gersbacher, Can Kattwinkel, Mario Sallat
Migrating to Kubernetes Part 4 – Create Environments via Gitlab
Written by: Pirmin Gersbacher, Can Kattwinkel, Mario Sallat
Radcup Part 2 – Transition into Cloud
Written by: Immanuel Haag, Christian Müller, Marc Rüttler Several steps are necessary to transfer the Radcup backend to the cloud and make it accessible to everyone from the outside. These are explained in more detail in the following sections.
Radcup Part 3 – Automation with Gitlab CI/CD
Written by: Immanuel Haag, Christian Müller, Marc Rüttler The goal of this blog entry is to automate the previously performed steps. At the end all manual steps should be automated when new code changes are added to the repository. The new version of the backend will be made available in the cloud at the end.
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