From model to production,
built to last.

MLOps applies to Machine Learning models the automation, deployment, monitoring and governance practices inspired by DevOps. It makes it possible to move from a model that works in experimentation to a reliable service in production.

As soon as a model is used by users or by business processes, you need to be able to version it, monitor its performance, roll back to a previous version and control its infrastructure. The need naturally grows with the number of models deployed.

DevOps focuses mainly on code and applications. MLOps must also handle datasets, features, model versions, ML metrics, data drift and retraining cycles.

Yes, provided the model, its code, its dependencies and the information needed to run it are available. We can audit what already exists, then build the packaging, deployment, monitoring and versioning chain around it.

We can monitor the data distribution, the quality of the predictions, errors, latency and other metrics specific to the use case. Alerts make it possible to identify drift or degradation before it becomes invisible to the teams.

Not necessarily. Depending on how critical they are, on the volume and on the availability of data, retraining can be automated, triggered by an alert or subject to human validation. We choose the level of automation suited to the risk.

Kubernetes, managed cloud services or dedicated infrastructures can all be relevant. The choice depends on volumes, latency, cost, in-house skills, security constraints and the need for portability.

We track CPU/GPU consumption, the number of predictions, unused resources, sizing, auto-scaling and the cost of external models, among others. The aim is to maintain the level of service required without oversizing the infrastructure.

That depends heavily on what already exists. Industrialising a single model with basic deployment and monitoring is very different from building a complete platform including a registry, a feature store, multiple environments, retraining and governance.

Yes, that can be part of the project. Documentation, skills transfer and team training make day-to-day operation independent, while keeping the option of external support for future developments.