Scalable machine learning models thanks to MLOps framework implementation
Meerlanden
Customer case
Data Engineering
B2B
Machine learning operations
Philip Roeleveld
Machine Learning Engineer
3 min
10 Feb 2025
After we built a data warehouse for Meerlanden, their data scientist began working with the data. We proposed setting up a Machine Learning Operations (MLOps) framework together, allowing them to integrate their models directly into the existing environment. This enabled them to make predictions that improved the efficiency of Meerlanden’s services.
Approach
We started with the first use case. Meerlanden is responsible for, among other things, waste collection in the municipality. For putting out wheelie bins, planning waste collection routes and internal reports, they wanted to know their addresses whether they are high-rise or low-rise type.
The data scientist had already developed the model and possessed the necessary knowledge and data. Using Databricks, we implemented an MLOps framework within the existing data warehouse. This framework enables the data scientist to automate and further optimise their models independently.
In close collaboration with Meerlanden's data scientist, we optimised the existing machine learning model to align it better with our MLOps framework.
We set up the framework to allow new models to be easily added and integrated the existing model into it.
Functionality was added to enable automatic model training based on new data, such as newly added addresses along a route.
We familiarised Meerlanden’s data scientist with the Databricks environment and provided support to help them organise their work within the MLOps framework.
Result
The MLOps framework is now fully operational, and Meerlanden can continue using it independently. The first model is already making automatic predictions, and the process of model improvement and production deployment has been greatly simplified. Predictions are continuously updated because the model is automatically trained with new data.
Additionally, we added functionality that allows new machine learning models to be added and tested at the push of a button. This has made the organisation better equipped for future innovations and the expansion of their AI capabilities.
Future
The MLOps framework lays the foundation for continued collaboration. Meerlanden now has a robust and flexible solution to elevate their data-driven innovations.
Looking ahead, we plan to tackle new challenges together. For example, when waste collection vehicles are on their routes, situations occasionally arise where some finish early and need to take over other routes. These unforeseen changes can disrupt planning. To address this, Meerlanden intends to use a clustering algorithm. This algorithm will provide insights into which vehicles can best support each other without disturbing the existing routes.
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