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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.
Which format do you prefer?
30 free minutes with a training advisor — no commitment.
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No-Code / Low-Code training in Leeds in November 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Master influence and persuasion skills for 2026 with proven strategies, emerging tech, and practical exercises tailored for professional growth in a dynamic world.
Professional Training training in New York in September 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Professional Training training in Memphis in October 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Don't let this gap widen
Sans maîtrise du déploiement et de la gestion de modèles de Machine Learning dans le Cloud, 75 % des projets IA échouent en phase de production, gaspillant en moyenne 150 000 € par initiative selon Gartner.
Ces échecs provoquent des downtimes critiques, avec des coûts horaires atteignant 10 000 € et 60 % des incidents cloud liés à une mauvaise scalabilité des modèles.
Votre entreprise perd ainsi 20 % de revenus potentiels face à la concurrence agile, exposant données sensibles et réputation à des risques majeurs.
Chaque retard mensuel creuse l'écart, menaçant directement votre carrière et la compétitivité globale.
The Maîtriser Cloud AI Platform : Déployer et Gérer des Modèles de Machine Learning dans le Cloud 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.
To ensure the quality of the Maîtriser Cloud AI Platform : Déployer et Gérer des Modèles de Machine Learning dans le Cloud training, Learni provides the following 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.
The assessment of skills acquired during the Maîtriser Cloud AI Platform : Déployer et Gérer des Modèles de Machine Learning dans le Cloud training is carried out through:
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.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Présentation de Cloud AI Platform, exploration de l’interface, comparaison avec d’autres solutions cloud, gestion de projet sur Google Cloud, stockage et préparation des datasets (BigQuery, Cloud Storage), bonnes pratiques d’organisation des données, automatisation des processus de préparation avec Dataflow ou Dataprep.
Utilisation de AI Platform pour le déploiement d’environnements, gestion des jobs d’entraînement, configuration des clusters (CPU/GPU/TPU), gestion de versions de modèles, monitoring du training, exploitation des notebooks gérés, gestion du budget cloud.
Déploiement des modèles en production (Endpoints & APIs), gestion du cycle de vie, versionning avancé, supervision & logs, monitoring en temps réel (Cloud Monitoring / Stackdriver), gestion de la scalabilité (auto-scaling), gestion des permissions et sécurité, audit, bonnes pratiques de maintenance, automatisation avec CI/CD cloud natif.
Target audience
Ingénieurs, Data Scientists et développeurs souhaitant industrialiser et déployer des modèles d’IA à l’échelle
Prerequisites
Bases en Machine Learning et bonne compréhension du cloud computing
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