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Training Model Distillation - Compress Efficient AI Models 2026

Ref: HUQ313
10 people max.
4375€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Master the basic principles of model distillation for professional AI skills
  • Develop compact, high-performing models adapted to edge computing constraints
  • Implement teacher-student distillation techniques in certified training
  • Optimize the size and speed of AI models for enterprise deployments
  • Design reproducible distillation pipelines using open-source tools
  • Evaluate the impact of distillation on accuracy and inference in a professional context

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

Don't let this gap widen

Why this program matters

  • Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.

  • Organizations that don't train their talents on key topics see their competitiveness drop.

  • Every quarter without training is a gap widening with competitors who invest.

  • The cost of inaction quickly exceeds that of well-targeted training.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Model Distillation Fundamentals: Theory and Environment Setup (PyTorch, TensorFlow)

Discover the key concepts of model distillation, from teacher-student knowledge transfer to compression without performance loss. Install a dedicated environment with PyTorch and TensorFlow, perform your first knowledge transfer on a simple model like MNIST, analyze distillation metrics through guided practical exercises, and produce an initial report on size and speed gains to visualize immediate business impact.

Module 2Supervised Model Distillation Techniques: Logits and Features Distillation (Real-World Cases)

Dive into logits and features distillation using real datasets like CIFAR-10. Configure a pre-trained teacher model and distill it to a compact student, experiment with softmax temperatures to balance accuracy and size, test on simulated edge hardware, generate visualizations of loss curves and performance comparisons, and apply to a business case to reduce cloud inference costs by 50%.

Module 3Advanced Beginner Model Distillation: Online and Offline Distillation (Hugging Face Tools)

Explore online and offline model distillation variants using Hugging Face transformers. Distill a BERT teacher to a DistilBERT student on NLP text, integrate regularizations to stabilize training, measure latency on mobile via ONNX, perform cross-benchmarks on CPU/GPU/edge, and produce a production-ready deployable model with complete documentation and unit tests.

Module 4Model Distillation Optimization 2026: Integrated Quantization and Pruning (TensorRT, ONNX)

Anticipate 2026 trends by combining distillation with quantization and pruning. Optimize a ResNet vision model using TensorRT and ONNX Runtime, reduce size by 70% while maintaining 95% accuracy, deploy on Raspberry Pi to simulate edge devices, analyze trade-offs using advanced profilers, and create an automated CI/CD pipeline for rapid team iterations in a business setting.

Module 5Model Distillation Deployment and Evaluation: Production and Monitoring (Docker, MLflow)

Finalize with deployment of your distilled models in Docker containers, integrate MLflow for experiment tracking and monitoring, evaluate in real conditions with A/B testing, prepare business reports on ROI (cloud savings up to 80%), defend your continuous thread project in front of the trainer, and leave with a certified portfolio ready to boost your professional AI career.

Evaluation method

  • Multiple-choice quiz to validate learning outcomes at the end of the training
  • Continuous assessment through practical exercises
  • Defense of the continuous thread project in front of the trainer

Learning method

  • Courses led by an active expert trainer
  • Practical exercises based on real business cases
  • Progressive continuous thread project throughout the training
  • Complete course materials provided to each participant

Methods, materials and delivery

The Training Model Distillation - Compress Efficient AI Models 2026 program is delivered onsite or remote (blended-learning, e-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training Model Distillation - Compress Efficient AI Models 2026 program, the following equipment is required:

  • Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
  • Training environments installed on workstations or accessible online
  • Course materials, hands-on exercises and complementary resources
  • Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training Model Distillation - Compress Efficient AI Models 2026 program is performed through:

  • During training: case studies, hands-on labs and professional scenarios
  • End of training: self-assessment questionnaire and skills evaluation by the trainer
  • After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.

Verified reviews

What our learners

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
Our method

Training quality, guaranteed at every step

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

  • Live briefing on goals and team context.
  • Veto right — we swap the trainer for free if needed.
Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

  • Hands-on cases drawn from your stack and projects.
  • Program co-written then validated by your team.
Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

  • NPS, knowledge quizzes and skills self-assessment.
  • Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

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