Data, model and experiment versioning
Experiment tracking and versions linked to data and code.
Data & artificial intelligence / MLOps
Your models, beyond the lab.
We build the full model lifecycle with you: data, training, evaluation, deployment and production monitoring.
How the pieces connect
01 / The context
When the model works in testing but everything else is missing.
Moving from a notebook to a service takes more than an endpoint. Results must be reproducible, versions controlled and changes in model quality understood.
- Turn isolated experiments into reproducible training.
- Integrate models into applications and business workflows.
- Detect data changes and review model performance.
02 / MLOps
What we build
Scope is agreed around your environment, priorities and team.
Training, evaluation and deployment pipelines
Automation with evaluation criteria before publication.
Production model monitoring and evolution
Quality, latency and consumption metrics with review procedures.
03 / How we work
We work as part of your team.
- 01
Understand the starting point
We review systems, objectives and constraints with the people who know the business. Together we agree priorities and how progress will be validated.
- 02
Build and validate together
We divide the work into reviewable deliveries and share decisions, code and documentation throughout execution.
- 03
Prepare for continuity
We validate the result, transfer knowledge and agree how to maintain and evolve what has been built.