What is the difference between DevOps and MLOps?

MLOps extends DevOps with ML-specific practices: data and model versioning, drift management, automatic retraining, and ML performance metrics.

Which MLOps tools do you recommend?

It depends on your context. For startups: MLflow + GitHub Actions. For enterprises: Kubeflow, SageMaker, or Vertex AI.

How do you handle model drift?

We set up continuous monitoring of data distributions (data drift) and performance (concept drift). Automatic alerts trigger either investigation or automatic retraining.

Can MLOps be done on-premise?

Yes, we can deploy MLOps platforms on your private infrastructure. Open source tools (Kubeflow, MLflow, Airflow) work well for this.

MLOPS

MLOps
ML Production Deployment

87% of Machine Learning projects never make it to production. MLOps is the discipline that bridges the gap between Data Science notebooks and reliable production systems. At NeuroVista, we are MLOps experts: we design and implement the pipelines, infrastructure, and processes that enable you to deploy, monitor, and maintain your ML models at scale.

Discuss your project Our approach

Concrete Use Cases

10x

Model deployment cycle acceleration (from weeks to hours)

99.9%

Prediction API availability thanks to resilient architecture

-80%

Debug time reduction through lineage and observability

50+

Models managed simultaneously on a centralized MLOps platform

< 5min

Detection and rollback time in case of performance degradation

Our Approach

We build MLOps platforms adapted to your maturity and constraints.

Audit & Scoping

Assessment of your current MLOps maturity, pain point identification, and target definition.

Proof of Concept (POC)

End-to-end pipeline setup on a pilot model. Technical architecture and tool validation.

MVP & Industrialization

Complete MLOps platform deployment: feature store, model registry, CI/CD pipelines, infrastructure as code.

Production & Optimization

Advanced monitoring, intelligent alerting, and cloud cost optimization. Team training and best practices documentation.

Deliverables

Documented MLOps architecture

Infrastructure as Code (Terraform, Pulumi)

ML CI/CD pipelines (GitHub Actions, GitLab CI)

Feature Store (Feast, Tecton)

Model Registry (MLflow, Weights & Biases)

Monitoring & Alerting (Prometheus, Grafana, custom)

Documentation and runbooks

Data/ML Engineering team training

Key Technologies

PyTorchJAXTransformersvLLMTensorRT-LLMTriton Inference ServerRayLangGraphLlamaIndexHugging FaceAWSGoogle CloudCloud RunKubernetesTerraformPulumiArgo CDGitHub ActionsIstioOpenTelemetry

Frequently Asked Questions

Get your models to production

Let's assess your MLOps maturity and define your roadmap.

Schedule an MLOps audit

MLOpsML Production Deployment