02 — AREAS OF EXPERTISE
AI & Big Data
Data pipeline integration, MLOps, AI model deployment. Data-driven strategy aligned with your business challenges.
The challenge
Many data and AI projects stall at the prototype stage: the model works on a laptop, but nobody knows how to feed it reliable data, deploy it, monitor it or evolve it. The value lies in the architecture around the model, not only in the model itself.
What I do
- Design data architectures: ingestion, storage, processing, governance.
- Build reliable, observable data pipelines.
- MLOps: industrialise model training, deployment and monitoring.
- Integrate AI components, including LLMs, into existing systems.
- Strategic framing: which use cases, which priorities, what level of investment.
How I work
We start with your business goals and the actual state of your data, before talking about tools. I favour short iterations that bring a first use case to production quickly, then generalise what works.
References
- SNCF — Big Data and data architecture.
- BNP Paribas — AI strategy consulting.
What you get
- A data and AI architecture that can go from prototype to production.
- Pipelines and models that are monitored, maintainable and documented.
- A realistic AI roadmap, prioritised by value.