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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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Don't let this gap widen
Sans maîtrise du déploiement et de l'optimisation de DeepSeek, vos projets IA subissent des pannes récurrentes et une consommation excessive de ressources.
Les entreprises perdent en moyenne 120 000 € par an en surcoûts cloud dus à une mauvaise optimisation de modèles open source comme DeepSeek.
65 % des échecs en production IA proviennent d'un déploiement défaillant, exposant l'entreprise à des pertes de données et une érosion rapide de sa compétitivité.
Chaque mois sans expertise, les responsables techniques risquent leur crédibilité et l'entreprise son avance stratégique face à des concurrents agiles.
The Maîtriser DeepSeek : Déploiement et Optimisation d’un Modèle IA Open Source 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 DeepSeek : Déploiement et Optimisation d’un Modèle IA Open Source 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 DeepSeek : Déploiement et Optimisation d’un Modèle IA Open Source 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.
Introduction à DeepSeek : contexte, philosophies open source, cas d’usages. Focus sur l’architecture : différences avec GPT et Llama, modèles disponibles, taille, gestion de la mémoire. Téléchargement, installation en environnement Python, préparation du GPU. Première interaction en ligne de commande et via interface web.
Implémentation d’inférences via API Python, scripts ou complément à des outils NLP existants. Construction d’applications conversationnelles et de génération de texte spécifiques métiers (RH, médical, industrie, etc.). Optimisations : choix des paramètres, configuration de l’utilisation GPU/CPU, gestion des batchs et pipelines. Introduction au fine-tuning : survol des méthodes (LoRA, adapters, etc.), préparation de datasets.
Déploiement en environnement cloud (AWS, Azure, GCP) et on-premise : scenarios, outils, benchmarks. Monitoring des performances, gestion de la charge. Sécurité : limitation des biais, contrôle d’accès, auditabilité. Considérations éthiques et RGPD. Table ronde sur les évolutions des modèles open source. Étude de cas pratique par équipe : conception d’un mini-projet DeepSeek (du proof of concept à l’intégration métier).
Target audience
Développeurs, data scientists, chefs de projets digitaux, ingénieurs en intelligence artificielle souhaitant exploiter ou intégrer DeepSeek dans leurs projets
Prerequisites
Maîtrise des bases en programmation Python, connaissance des fondements du machine learning, notion de traitement du langage naturel (NLP)
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