Delivery
Go-live becomes reproducible and suited to the project's validation requirements.
MLOPS
MLOps
models that live in production
MLOps connects data, models, deployment and operations. We design the pipelines, infrastructure and review practices that help a model evolve with its use.
What MLOps changes
Reliability
Load, failure and recovery scenarios can guide service choices.
Traceability
Every prediction traces back to the data and model that produced it.
Scale
The platform can organise several models, datasets and environments in a coherent framework.
Reaction
A detected drift triggers the alert, the rollback or the retraining.
Our Approach
We build MLOps platforms aligned with your actual maturity, not a conference ideal.
- 01
Audit & Scoping
Assessment of your MLOps maturity, friction points and target. Existing tools and team skills are part of the equation.
- 02
Proof of Concept (POC)
An end-to-end pipeline on a pilot model. Architecture and tooling get validated on real work.
- 03
MVP & Industrialization
Complete platform: feature store, model registry, CI/CD pipelines, infrastructure as code. Existing models migrate progressively.
- 04
Production & Optimization
Advanced supervision, useful alerts, cloud costs under control. Your teams take over, documentation in hand.
Deliverables
- 01Documented MLOps architecture
- 02Infrastructure as Code (Terraform, Pulumi)
- 03ML CI/CD pipelines (GitHub Actions, GitLab CI)
- 04Feature Store (Feast, Tecton)
- 05Model Registry (MLflow, Weights & Biases)
- 06Supervision and alerting (Prometheus, Grafana)
- 07Documentation and runbooks
- 08Data and ML Engineering team training
Key Technologies
PyTorchJAXTransformersvLLMTensorRT-LLMTriton Inference ServerRayLangGraphLlamaIndexHugging FaceAWSGoogle CloudCloud RunKubernetesTerraformPulumiArgo CDGitHub ActionsIstioOpenTelemetry
Frequently Asked Questions
NeuroVista, Paris
Your models deserve production
Let's assess your MLOps maturity and draw the roadmap.
Schedule an MLOps audit