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An effective MLOps workflow developed within the Datalab team

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The client,

Waternet is the only water company in the Netherlands responsible for the entire water cycle. It supplies drinking water in and around Amsterdam. In the area served by Waterschap Amstel Gooi en Vecht, it also works to maintain strong dykes and clean water.

Client Waternet
Industry Energy & Utilities
Services AI Solutions
Tools & techniques Azure (DevOps), Bicep, Python

Our solution,

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.
Koen Senior Data Scientist - Data Science Lab

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