AI & Generative AI

MLOps & LLMOps

Pipelines, evaluation harnesses, monitoring and cost controls that keep models accurate and affordable over time.

Vue d’ensemble

A model that works on launch day can degrade quietly as data, users and upstream systems change. MLOps and LLMOps provide the engineering discipline to keep AI solutions accurate, secure and affordable once they are in production.

For machine learning, we put in place versioned data and features, reproducible training pipelines, a model registry, automated deployment and monitoring for performance and drift. For generative AI, we add prompt and configuration versioning, evaluation harnesses that run on every change, tracing of model and tool calls, and guardrails for safety and data protection.

Cost is treated as a first-class metric. We instrument token usage and infrastructure spend, then apply caching, model routing and right-sizing so costs stay predictable as adoption grows. These practices are designed to fit your existing DevOps tooling and platform team, so AI is released and operated like any other well-run software.

Ce que vous obtenez

01MLOps and LLMOps reference architecture and standards
02CI/CD pipelines for training, evaluation and deployment
03Model and prompt registry under version control
04Monitoring dashboards for quality, drift, latency and cost
05Runbooks and on-call guidance for AI incidents

Technologies que nous maîtrisons

MLflow
Databricks
Amazon SageMaker
Azure Machine Learning
Kubernetes
GitHub Actions
LangSmith
Langfuse

Nous sommes indépendants des éditeurs : les outils sont choisis en fonction de vos besoins et de vos investissements existants.

Les questions que l’on nous pose souvent

Do we need a dedicated MLOps platform?

Not always. Many organizations get most of the benefit from the cloud and data platforms they already run, plus a few targeted tools for evaluation and tracing. We recommend the lightest setup that meets your scale and control requirements.

How do you keep generative AI costs under control?

We measure cost per request and per user, then apply techniques such as prompt optimization, response caching, routing simpler requests to smaller models and usage budgets. Cost dashboards make spend visible to the teams that drive it.