Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Formation Google Vertex AI - Déployer des modèles ML en production training are: Expérience en Python, machine learning basique (TensorFlow ou PyTorch), notions Google Cloud Platform. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Google Vertex AI - Déployer des modèles ML en production training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Formation Google Vertex AI - Déployer des modèle... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

Configure your training

Response within 24hNo commitment100% free

9 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Formation Google Vertex AI - Déployer des modèles ML en production training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Formation Google Vertex AI - Déployer des modèles ML en production training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Formation Google Vertex AI - Déployer des modèles ML en production training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Maîtriser Vertex AI pour orchestrer pipelines MLOps professionnels

Développer et entraîner modèles ML scalables sur Vertex AI

Implémenter déploiement automatisé de modèles en production certifiante

Optimiser monitoring et gestion de versions dans Vertex AI

Concevoir workflows end-to-end pour compétences enterprise MLOps

Intégrer Vertex AI à des environnements CI/CD d'entreprise

Training program

Module 1Vertex AI : pipelines MLOps et environnement de développement (GCP, Vertex AI Workbench)

Plongez dans l'écosystème Vertex AI avec création d'un workspace dédié, configuration d'environnements Jupyter via Vertex AI Workbench, exploration des services intégrés comme Pipelines et Model Registry, réalisation de votre premier pipeline MLOps end-to-end sur datasets réels d'entreprise, exercices pratiques pour automatiser ingestion de données et preprocessing ML, production d'un livrable fonctionnel prêt pour itération, avec focus sur scalabilité et sécurité professionnelle.

Module 2Vertex AI : entraînement et tuning de modèles (AutoML, custom training jobs)

Maîtrisez l'entraînement distribué sur Vertex AI en lançant des jobs custom avec TensorFlow et PyTorch, utilisation d'hyperparameter tuning pour optimiser performances, comparaison AutoML versus training manuel sur cas concrets comme classification d'images ou prédiction temps réel, exercices hands-on pour scaler sur GPU/TPU, génération de métriques détaillées et artefacts Model Garden, création d'un modèle optimisé déployable en entreprise, valorisant directement vos compétences MLOps certifiantes.

Module 3Vertex AI : déploiement et serving de modèles (Endpoints, online/offline prediction)

Déployez vos modèles en production via Vertex AI Endpoints avec configuration scaling automatique, tests de prédiction batch et online sur traffic simulé entreprise, intégration monitoring drift et explainability via Vertex Explainable AI, exercices pratiques pour A/B testing modèles, gestion rollbacks et traffic splitting sécurisés, production d'un endpoint live connecté à une API REST, démontrant valeur immédiate pour workflows professionnels et réduction temps-to-market.

Module 4Vertex AI : monitoring avancé et CI/CD MLOps (Vertex Pipelines, integrations GitHub Actions)

Orchestrez CI/CD complet avec Vertex Pipelines et integrations GitHub/GitLab, implémentez monitoring continuous via Vertex Model Monitoring pour détecter dégradations, exercices sur remediation automatisée et retraining triggers, cas d'étude entreprise avec full lifecycle MLOps, finalisation projet fil rouge avec déploiement prod-like, revue de code et optimisation coûts GCP, obtenant compétences certifiantes pour booster carrière en IA scalable.