General Presentation
The development of a high-performing artificial intelligence model is just the first step. The true value of AI can only be realized through its ability to be integrated, operated, maintained, and improved in production, within concrete business environments, under conditions of reliability, traceability, security, and scalability.
That is why NeuriaLabs deploys advanced expertise in MLOps (Machine Learning Operations): an emerging discipline at the intersection of AI development, software engineering, and system administration. Inspired by DevOps practices, our approach aims to automate the entire lifecycle of AI models, from their initial training to their decommissioning, ensuring a smooth, controlled, and reproducible transition between research and operational deployment.
CI/CD Pipeline Tailored for AI
We implement CI/CD (Continuous Integration / Continuous Deployment) pipelines specifically designed for artificial intelligence artifacts :
• Continuous Integration (CI): automation of unit and functional tests on AI components (data preprocessing, feature engineering, algorithms, scripts), verification of compliance with coding standards, version control of models, and triggering of conditional trainings.
• Continuous Deployment (CD): automated validation and promotion of models to staging and then production environments, using tools such as GitLab CI, GitHub Actions, Jenkins, or MLflow, with Dockerized packaging and orchestrated deployment via Kubernetes or managed servers.
We ensure complete traceability of each model version, including training data, hyperparameters, test performances, deployment timestamp, and comments from data scientists.
Monitoring of Models in Production (Monitoring and Drift)
Once in production, models can see their performance degrade due to various factors: evolving data, behavioral drift of users, or contextual or structural changes in the business environment.
That is why we integrate continuous performance monitoring tools for models, capable of detecting :
• Data drifts: changes in the distribution of input variables.
• Concept drifts: changes in the relationship between input variables and the expected output.
• Performance degradations: decrease in accuracy rate, increase in critical errors, lengthening of response times.
These metrics are visualized in intelligent dashboards (Evidently, Prometheus, Grafana, Kibana) and can automatically trigger alerts, re-training, or controlled deactivations.
Industrialization of Models (Serving and Orchestration)
We ensure the availability of AI models as services accessible via API, with high availability, low latency, and interoperability with client systems. Our models are deployed according to the following modalities :
• Synchronous or asynchronous serving: in real-time or in batch, via secure RESTful endpoints.
• Containerization and orchestration: packaging into Docker images, deployment via Kubernetes, Knative, or serverless frameworks.
• Versioning and automatic rollback: management of multiple versions of models in parallel, with the possibility of returning to an earlier version in case of detected malfunction.
• A/B testing and canary deployment: experimental validation of new models in production, on a subset of the traffic.
This approach allows for controlled experimentation, gradual scaling, and continuous improvement of performance.
Collaboration, Documentation, and Governance of Models
AI projects mobilize multidisciplinary teams (data scientists, engineers, business, security, governance). We therefore set up tools that promote smooth and audited collaboration :
• Centralized model repositories (MLflow Model Registry, SageMaker Model Hub)
• Versioned and commented notebooks (Jupyter, Databricks, Colab Enterprise)
• Automated documentation (Sphinx, MkDocs, exported notebooks)
• Collaborative workspaces and traceability of experiments (Weights & Biases, Neptune, DVC)
Each model is accompanied by a complete governance dossier, including :
• Training assumptions
• Cross-validation results
• Analysis of potential biases
• Compliance with regulatory or ethical requirements
• Update recommendations
NeuriaLabs Approach
Our philosophy regarding MLOps is based on a simple conviction: a model, no matter how performant, is only useful if it is properly operated, supervised, improved, and controlled.
To this end, we commit to :
• Industrialize AI workflows with a high degree of automation, robustness, and flexibility;
• Bring together the worlds of data science and system engineering to avoid silos and technical frictions;
• Make models explainable, governable, and ethically responsible throughout their complete lifecycle;
• Adapt MLOps pipelines to the enterprise scale, whether in an R&D lab, a business team, or a global organization.
