[ Tech stack ]
Azure Machine Learning
Azure's managed ML platform, with native MLflow tracking and Entra ID integration.
Azure Machine Learning organizes work in workspaces: notebooks and managed compute, AutoML and a drag-and-drop designer, jobs and pipelines defined in YAML or the Python SDK, MLflow-native tracking and model registry, then managed online and batch endpoints with blue-green deployment. It relies on Entra ID for identities and roles, on Azure DevOps or GitHub Actions for MLOps, and on Azure OpenAI and the Azure AI Foundry catalog for generative AI. It's our choice when the client's IT is standardized on Microsoft.
[ Why Azure Machine Learning at Dexon ]
What this technology does well, and why we use it.
Typical usage: ML inside a Microsoft IT estate, governed MLOps, forecasting and scoring, predictive maintenance.
- 01
MLflow-native tracking and registry: existing MLflow code works as is.
- 02
AutoML and visual designer to prototype, SDK and YAML to industrialize.
- 03
Managed online and batch endpoints, blue-green deployment and traffic splitting.
- 04
Entra ID, RBAC, private networking and Azure DevOps: MLOps that fits corporate IT.
[ Complementary technologies ]
The building blocks we often mobilise alongside.
A stack rarely exists alone. Here are the technologies Dexon most often pairs with this one, through pipeline habits, usage similarity or internal mastery. Click on a building block to see its scope.
[ Reassurance ]
- 0+
- custom projects delivered
- 30
- engineers, designers, project managers
- 80 %
- from top French schools
- 24 h
- average reply time
[ They trust us ]
More than 100 French and European companies trust us
[ Press ]
They talk about us.
Nationwide coverage by BFM Business, Le Figaro, Challenges, La Tribune and CNews. An outside reading of our work and our innovations.
