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 Vertex AI - Déployer des pipelines ML scalables training are: Expertise en Machine Learning avec TensorFlow/PyTorch, Python avancé, bases de Google Cloud Platform et conteneurs Docker. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Vertex AI - Déployer des pipelines ML scalables training is available in présentiel. The format can be adapted to your needs (inter, intra, custom).

Formation Vertex AI - Déployer des pipelines ML ... | 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.

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Methods, approaches, and teaching resources

The Formation Vertex AI - Déployer des pipelines ML scalables 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 Vertex AI - Déployer des pipelines ML scalables 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 Vertex AI - Déployer des pipelines ML scalables 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 des pipelines ML professionnels en entreprise

Développer des workflows automatisés certifiants avec Vertex Pipelines

Implémenter des déploiements scalables de modèles IA en production

Optimiser la gestion d'état et le monitoring avec Vertex AI tools

Concevoir des solutions explainables et sécurisées pour des compétences expert

Intégrer Vertex AI dans des environnements cloud hybrides d'entreprise

Training program

Module 1Pipelines Vertex AI : concevoir des workflows ML automatisés (Vertex Pipelines, Kubeflow, datasets avancés)

Plongez dans la création de pipelines end-to-end avec Vertex AI Pipelines, configurez des composants personnalisés en Python, intégrez Kubeflow pour l'orchestration avancée, manipulez des datasets massifs via Vertex AI Feature Store, réalisez des exercices pratiques sur des cas d'entreprise réels comme la prédiction de churn client, testez l'automatisation des hyperparamètres avec Vertex AI Hyperparameter Tuning, produisez un livrable pipeline déployable et optimisé pour scaler en production.

Module 2Déploiement Vertex AI : monitorer et sécuriser des modèles en prod (Endpoints, Explainable AI, MLOps)

Déployez des modèles sur Vertex AI Endpoints avec autoscaling et traffic splitting, intégrez Explainable AI pour l'interprétabilité des prédictions, configurez Vertex AI Monitoring pour détecter les dérives en temps réel, appliquez des pratiques MLOps avancées avec CI/CD GitHub Actions, simulez des scénarios d'entreprise critiques comme la détection de fraudes, générez des rapports de performance chiffrés, finalisez un projet fil rouge avec dashboard personnalisé Vertex AI, prêt pour une intégration immédiate en entreprise certifiante.