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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.
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Cybersecurity training in Oklahoma City in December 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
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Don't let this gap widen
Sans maîtrise d'Amazon SageMaker, 85 % des projets Machine Learning sur AWS restent bloqués en phase prototype, gaspillant des mois précieux.
Les erreurs de déploiement manuel coûtent en moyenne 150 000 € par incident, avec 40 % des fuites de données cloud liées à une mauvaise industrialisation ML.
Votre entreprise perd 30 % de parts de marché face aux concurrents agiles, tandis que votre carrière stagne face à l'explosion des besoins en expertise AWS.
Chaque mois sans compétences solides amplifie ces risques, menaçant la viabilité business.
The Maîtriser Amazon SageMaker : Développement et Déploiement de Modèles Machine Learning dans le Cloud AWS 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 Amazon SageMaker : Développement et Déploiement de Modèles Machine Learning dans le Cloud AWS 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 Amazon SageMaker : Développement et Déploiement de Modèles Machine Learning dans le Cloud AWS 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.
Panorama des services AWS pour le Machine Learning, présentation de SageMaker, architecture et intégration dans un workflow existant. Atelier pratique : navigation dans l’interface SageMaker Studio, gestion des comptes AWS. Utilisation d’Amazon S3 pour l’import et la gestion sécurisée des datasets, transformation de données avec SageMaker Data Wrangler, exploration des outils de Data Preparation, gestion des permissions et sécurité.
Création et configuration des environnements de développement Jupyter Notebook dans SageMaker. Entraînement de modèles supervisés avec les algorithmes natifs SageMaker (XGBoost, Linear Learner, etc.) et frameworks open-source (TensorFlow, PyTorch, Scikit-learn). Utilisation de SageMaker Autopilot pour l’AutoML. Démonstration de pipelines d’entraînement automatisés, gestion des versions de modèles (MLOps).
Déploiement de modèles en endpoints temps-réel ou batch, gestion du scaling automatique, stratégies de déploiement Zero-downtime (canary, blue/green). Monitoring du modèle (CloudWatch, SageMaker Model Monitor), détection des dérives de données, mise à jour continue des modèles en production. Contrôle des coûts et optimisation des ressources cloud. Sécurité et meilleures pratiques pour l’industrialisation de bout en bout.
Target audience
Data Scientists, Développeurs, Ingénieurs Cloud, Chefs de projet technique souhaitant industrialiser le Machine Learning sur AWS
Prerequisites
Bases en Python, connaissance des fondamentaux du Machine Learning, notions de cloud computing souhaitées
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