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 Blended Learning - Optimiser l'apprentissage ML mixte training are: Bases en Machine Learning, notions de pédagogie digitale, expérience en formation entreprise. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Blended Learning - Optimiser l'apprentissage ML mixte training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Formation Blended Learning - Optimiser… | 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

8 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Formation Blended Learning - Optimiser l'apprentissage ML mixte 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 Blended Learning - Optimiser l'apprentissage ML mixte 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 Blended Learning - Optimiser l'apprentissage ML mixte 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 le blended learning pour former efficacement aux compétences Machine Learning en entreprise

Développer des parcours mixtes intégrant TensorFlow et PyTorch dans des formations certifiantes

Concevoir des modules MLOps adaptés à un apprentissage hybride professionnel

Implémenter des outils blended pour booster la montée en compétences des équipes IA

Optimiser l'engagement apprenant via des pratiques blended en contexte ML

Évaluer l'impact des formations blended sur la performance entreprise en MLOps

Training program

Module 1Fondamentaux Blended Learning : principes et intégration ML (TensorFlow intro)

Découvrez les bases du blended learning appliquées au Machine Learning, explorez les modalités synchrone/asynchrone avec TensorFlow pour des exercices pratiques sur datasets simples, concevez votre premier parcours mixte via plateformes LMS, analysez des cas d'entreprise réussis, produisez un plan blended initial avec livrables concrets et feedback immédiat des experts.

Module 2Conception Blended Learning : modules PyTorch et interaction (outils collaboratifs)

Plongez dans la création de contenus blended spécifiques PyTorch, intégrez vidéos interactives, quizzes adaptatifs et sessions live, simulez des ateliers mixtes sur modèles neuronaux, testez l'engagement via gamification, développez des ressources réutilisables pour formations MLOps, générez un prototype de module avec métriques de suivi apprenant.

Module 3Implémentation Blended Learning : MLOps pipelines (déploiement hybride)

Apprenez à déployer des pipelines MLOps en mode blended, combinez coding sessions distanciel avec reviews asynchrones, pratiquez sur TensorFlow Serving et PyTorch Lightning, intégrez monitoring tools comme MLflow, résolvez cas réels d'entreprise, produisez un workflow blended opérationnel avec dashboards de performance et rapports d'impact.

Module 4Optimisation Blended Learning : data-driven ML (TensorFlow, PyTorch avancés)

Optimisez vos parcours blended via analytics learning, analysez données d'engagement avec TensorFlow et PyTorch pour personnalisation, ajustez contenus en temps réel sur feedbacks, explorez IA pour adaptive learning en MLOps, testez A/B sur cohorts réelles, livrez un plan d'optimisation chiffré avec ROI projeté pour entreprise.

Module 5Évaluation et Scaling Blended Learning : MLOps en production (certification)

Finalisez avec évaluation 360° des blended learning ML, mesurez compétences acquises via projets TensorFlow/PyTorch, scalez pour équipes enterprise avec MLOps, préparez certifications Qualiopi, review stratégique des parcours, produisez portfolio complet et plan d'action post-formation pour déploiement immédiat en entreprise.