An effective MLOps workflow developed within the Datalab team
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The Waternet Datalab team aims to deliver projects that are actually put into practice. A robust MLOps workflow is essential to this. As part of the Datalab team, we set up the MLOps workflow and continue to maintain it. This includes an MLflow model registry, CI/CD templates, and deploying code and models to production. For example, we developed an ML model to minimise nitrous oxide emissions at a wastewater treatment plant. We are now developing a model to reduce the use of iron chloride in the pretreatment of river water for drinking water production.
It feels good to develop and maintain solutions that have a direct impact on society. Every project the team works on is intended to reach production, which makes the ML engineering work particularly interesting.
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