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 Training Model Distillation - Optimize AI for Efficient Production training are: Proficiency in Python, PyTorch or TensorFlow, fundamentals of deep learning and model training. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Model Distillation - Optimize AI for Efficient Production training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Model Distillation - Optimize AI for Ef... | 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 Training Model Distillation - Optimize AI for Efficient Production 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 Training Model Distillation - Optimize AI for Efficient Production 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 Training Model Distillation - Optimize AI for Efficient Production 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

Master model distillation techniques to compress professional AI models

Develop efficient distillers adapted to business constraints

Implement knowledge distillation with advanced loss functions

Optimize distilled model performance for production deployment

Design automated and certified distillation pipelines

Evaluate and benchmark distilled models in real business contexts

Training program

Module 1Fundamentals of Model Distillation: Principles and Environment Setup (PyTorch, Hugging Face)

Dive into the basics of professional model distillation, install the environment with PyTorch and Hugging Face transformers, explore teacher-student paradigms on real business cases, complete your first simple distillation exercise from BERT to a lightweight model, analyze fidelity and compression metrics, and produce an initial report on speed and memory gains to validate the approach.

Module 2Advanced Model Distillation: Loss Functions and Hybrid Architectures (KL Divergence, Feature Matching)

Deepen key loss functions like KL Divergence and Mean Squared Error for precise distillation, configure hybrid teacher-student architectures on real business datasets, practice supervised and unsupervised training with guided exercises, test on NLP and vision tasks, generate convergence visualizations, optimize hyperparameters via grid search, and document precision improvements for scalable deployments.

Module 3Model Distillation Optimization: Integrated Quantization and Pruning (TensorRT, ONNX)

Integrate post-distillation quantization and structured pruning for ultra-lightweight models, export to ONNX and TensorRT for fast inference, apply to a capstone business project with edge computing constraints, measure real inference speedups, perform before/after benchmarks, adjust to minimize performance loss, and prepare a production-ready optimized model deliverable with certified metrics.

Module 4Model Distillation Deployment: CI/CD Pipelines and Monitoring (Docker, MLflow)

Build end-to-end model distillation pipelines with Docker and MLflow for professional traceability, deploy on Kubernetes or AWS/GCP cloud, integrate drift monitoring and automated retraining, test in real business conditions with A/B testing, evaluate business impact via ROI calculation, finalize your capstone project with source code and interactive dashboard, and prepare a defense to certify your AI optimization skills.