Keep track of performance
Monitoring shows whether a solution is still working as intended.
MLOps
Move machine learning models from development into everyday use while keeping them manageable.
A model may perform well in a test and then deteriorate as data or context changes. Without monitoring and version control, this often goes unnoticed. MLOps organises the development, deployment, monitoring and maintenance of machine learning models.
From an isolated possibility to a practical application
An application only becomes meaningful when it fits a specific task and the information available to carry it out.
How we set it up depends on the application’s complexity and risk. That is why we define in advance what the solution should and should not do, how its output will be checked and who remains responsible.
What it delivers
Address changes promptly
Changes to data, models or systems can be assessed in a targeted way.
Clear management
Agreements set out who is responsible and how incidents and improvements are handled.
Our approach
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Assess importance and risk
We determine how critical the solution is to the organisation.
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Agree on management arrangements
We define responsibilities, indicators and follow-up procedures.
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Set up monitoring
We monitor relevant performance, data and operational signals.
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Handle incidents and changes
We organise recovery and assess changes.
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Make targeted improvements
We improve the solution based on how it is used and measurable needs.
Designed to fit your organisation
This service is relevant to organisations that use machine learning models in production. The exact setup depends on the process, the available data and the risks associated with the application.
We therefore start with a clearly defined application and only expand once the initial setup works in practice.
Other solutions
One data platform for fragmented sourcesBring data from separate systems together in one place, where everyone sees the same figures.
Dashboard for day-to-day operationsSee how things stand on one screen, without having to build reports first.
Predicting when maintenance is neededPlan maintenance based on what the data says, not on a fixed schedule.
Frequently asked questions
This service focuses on the following challenge: MLOps organises the development, deployment, monitoring and maintenance of machine learning models. How it works in practice depends on the process and the available data.
The service may be relevant to organisations that use machine learning models in production. A specific need and usable information matter more than the size of the organisation.
That depends on the application. We start with the question that needs to be addressed, then determine which data or sources are necessary and suitable.
No. For fixed, predictable tasks, standard automation may be enough. We choose the simplest approach that addresses the challenge effectively and add complexity only when needed.
We define the scope of the application, check the results with users and put appropriate controls in place. Once it is in use, we monitor how it works and adapt the solution when the data, usage or context changes.
Discover what MLOps could mean for your organisation
Want to explore where it makes sense to start with MLOps? Together, we map out the challenge, the available information and the constraints.