FORMATION IA
MLOps pour les équipes IA en production
Construire et opérer des pipelines ML fiables, de l'expérimentation à la production, avec les outils MLOps modernes.
Ce qu'elle couvre
Ce programme de niveau praticien couvre l'ensemble du cycle de vie MLOps : CI/CD pour les modèles, feature stores, registres de modèles, infrastructure de serving et supervision en production. Les participants réalisent des travaux pratiques pour déployer de vrais pipelines avec des outils standard du marché tels que MLflow, Kubeflow et Feast. Le cours aborde la détection de dérive, les déclencheurs de réentraînement automatisé, les stratégies de rollback et les exigences de gouvernance. À l'issue de la formation, les équipes sont en mesure de concevoir et d'opérer une plateforme ML de niveau production adaptée à leur maturité.
À l'issue, vous saurez
- Design and implement a CI/CD pipeline that automatically trains, validates, and deploys an ML model on code or data changes
- Configure a feature store to serve low-latency features consistently across training and inference environments
- Set up a model registry with versioning, stage transitions, and approval gates using MLflow
- Instrument a deployed model with drift detection alerts and an automated retraining trigger
- Execute a safe rollback from a degraded model version using a blue/green or canary deployment strategy
Sujets abordés
- CI/CD pipelines for model training and deployment
- Feature stores: design, ingestion, and serving (Feast, Tecton)
- Model registries and versioning with MLflow and DVC
- Model serving patterns: batch, real-time, shadow and canary deployments
- Production monitoring: data drift, concept drift, and performance degradation
- Automated retraining triggers and pipeline orchestration (Airflow, Kubeflow Pipelines)
- Rollback strategies and blue/green deployments
- Governance, lineage tracking, and audit trails
Modalité
Delivered as a 3–5 day intensive bootcamp, available in-person or remote-live. Each day combines 40% concept sessions with 60% hands-on labs on a shared cloud environment (AWS or GCP). Participants receive a pre-configured lab repo, reference architecture diagrams, and a post-bootcamp Slack channel for 30-day follow-up support. In-person delivery recommended for teams co-building a shared platform.
Ce qui fait que ça marche
- Assign a dedicated ML platform owner who maintains tooling standards and onboards new model owners
- Define and automate model quality gates (accuracy thresholds, bias checks) as part of the CI pipeline from day one
- Start with a single end-to-end reference pipeline on a real use case before generalising to a platform
- Establish a shared model registry and naming convention so all teams discover and reuse existing model assets
Erreurs fréquentes
- Treating model deployment as a one-off script rather than a reproducible, versioned pipeline
- Skipping feature store adoption and duplicating feature logic between training and serving, causing training-serving skew
- Monitoring only infrastructure metrics (CPU, latency) and missing model-level drift until business impact is visible
- Over-engineering the MLOps stack before validating that the use case justifies the operational complexity
Quand NE PAS suivre cette formation
A team that has fewer than two models in production and no dedicated ML engineer: the overhead of a full MLOps stack will stall delivery rather than accelerate it — a lightweight experiment-tracking setup (MLflow alone) is sufficient at that stage.
Fournisseurs à considérer
Sources
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