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 Supervised Fine-Tuning SFT - Optimiser modèles IA Big Data training are: Bases en Python, machine learning supervisé, manipulation de datasets volumineux avec Pandas ou Spark, notions de transformers.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Supervised Fine-Tuning SFT - Optimiser modèles IA Big Data training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Formation Supervised Fine-Tuning SFT - 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

7 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Formation Supervised Fine-Tuning SFT - Optimiser modèles IA Big Data 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 Supervised Fine-Tuning SFT - Optimiser modèles IA Big Data 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 Supervised Fine-Tuning SFT - Optimiser modèles IA Big Data 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 les techniques de Supervised Fine-Tuning (SFT) pour affiner modèles IA sur datasets professionnels.

Développer des compétences en préparation de données Big Data pour entraînements SFT certifiants.

Concevoir des pipelines SFT intégrant Hadoop et Data Lakes pour scalabilité entreprise.

Implémenter des stratégies d'optimisation SFT réduisant coûts computationnels de 40%.

Déployer des modèles SFT fine-tunés en production avec monitoring avancé.

Évaluer l'impact SFT sur performances métier via métriques précises.

Training program

Module 1Fondamentaux Supervised Fine-Tuning : protocoles SFT sur datasets structurés (Hugging Face, PyTorch)

Découvrez les principes essentiels du Supervised Fine-Tuning (SFT) via ateliers pratiques sur datasets Big Data, configurez environnements PyTorch et Hugging Face, préparez corpus annotés avec outils comme LabelStudio, testez premiers fine-tunings sur modèles BERT, analysez courbes d'apprentissage, produisez rapports initiaux de convergence, intégrez feedbacks pour itérations rapides, tout en simulant cas entreprise réels pour une formation professionnelle immersive.

Module 2Préparation données SFT : ingestion Hadoop et Data Lake (Spark, Delta Lake)

Plongez dans l'extraction et nettoyage de volumes massifs via Hadoop et Spark, structurez Data Lakes pour SFT optimal, appliquez transformations ETL sur téraoctets de données, gérez imbalances classes avec SMOTE avancé, validez qualité via pipelines automatisés, générez subsets d'entraînement labellisés, simulez flux réels d'entreprise, exportez formats compatibles transformers, obtenez livrables prêts pour fine-tuning, renforçant compétences Big Data certifiantes.

Module 3Implémentation SFT avancée : hyperparamètres et LoRA (PEFT, Accelerate)

Configurez hyperparamètres SFT pour convergence rapide, intégrez LoRA via PEFT pour efficacité mémoire sur GPU clusters, entraînez sur datasets Big Data avec Accelerate, monitorez via Weights & Biases, optimisez batch sizes et learning rates, testez early stopping, comparez baselines vs fine-tunés, produisez métriques F1-score supérieures à 90%, appliquez à cas métier comme NLP entreprise, livrez notebooks reproductibles pour équipes.

Module 4Optimisation SFT Big Data : scaling distribué (DeepSpeed, Ray)

Maîtrisez scaling SFT sur clusters via DeepSpeed ZeRO et Ray Train, distribuez entraînements sur Data Lakes Hadoop, réduisez temps de 70% via sharding, gérez multi-GPU avec mixed precision, intégrez monitoring distribué, testez robustesse sur datasets noisy, raffinez via active learning, déployez prototypes en conteneurs Docker, analysez coûts AWS/GCP, obtenez pipelines scalables pour production entreprise, certifiant compétences avancées.

Module 5Déploiement et évaluation SFT : MLOps intégrés (MLflow, Kubernetes)

Déployez modèles SFT fine-tunés via MLflow et Kubernetes, configurez APIs FastAPI pour inférence Big Data, intégrez monitoring Drift avec Evidently, A/B testez versions, mesurez ROI métier chiffré, automatisez retraining sur Hadoop streams, sécurisez avec tokenization, produisez dashboards Grafana, validez conformité RGPD, finalisez portfolio projets réels, concluez par certification compétences SFT professionnelles pour carrière boostée.