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Training Model Distillation - Compress ML Models for Production

Ref: NMZ838
10 people max.
5500€ 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 fundamental principles of model distillation in certified professional training
  • Develop practical skills to distill complex models into lightweight versions
  • Implement distillation techniques with TensorFlow in a business context
  • Set up MLOps pipelines adapted to PyTorch for efficient deployments
  • Optimize the performance of distilled models to reduce inference costs
  • Design real-world projects integrating model distillation in professional environments

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 Key Concepts (Python, TensorFlow Basics)

Discover the theoretical basics of model distillation, an essential technique for transferring knowledge from a massive teacher model to a compact student model, using Python and initial TensorFlow tools. Participate in guided exercises on preparing simple datasets, pre-distillation performance analysis, and visualize potential gains in size and speed through concrete image classification cases, with deliverables like an initial evaluation report.

Module 2Model Distillation Implementation: First Practical Attempts (PyTorch, Loss Functions)

Dive into the practical implementation of model distillation with PyTorch, configuring specific loss functions like KL divergence to align teacher-student predictions. Conduct hands-on workshops on basic CNN models, test various distillation ratios, measure impact on accuracy and latency, and produce a first functional distilled model ready for MLOps integration, with personalized trainer feedback.

Module 3Advanced Model Distillation: Advanced Optimization (TensorFlow, Hyperparameters)

Deepen model distillation optimization via TensorFlow, exploring hyperparameters like temperature and alpha to maximize fidelity. Apply these methods to real datasets like CIFAR-10, integrate multi-stage knowledge distillation techniques, evaluate precision/speed trade-offs, and generate convergence visualizations, resulting in a reusable scripted pipeline for business.

Module 4MLOps Integration for Model Distillation: Deployment and Monitoring (PyTorch, Docker)

Integrate model distillation into complete MLOps workflows with PyTorch and tools like Docker for containerization. Deploy distilled models on edge servers, set up monitoring with Prometheus for traceability, simulate production scenarios with variable loads, test resilience and scalability, and finalize a deployment dossier including ready-to-use REST APIs.

Module 5Applied Model Distillation Projects: Real Business Cases (Mixed TensorFlow, PyTorch)

Consolidate your skills through a capstone project on model distillation applied to a concrete business case, such as optimizing an NLP model for mobile, mixing TensorFlow and PyTorch. Develop from A to Z, from training to final evaluation, integrate peer and expert feedback, produce a professional portfolio with source code, quantified metrics, and MLOps recommendations for production deployment.

Evaluation method

  • Daily interactive quizzes on model distillation theory and practice
  • Hands-on projects evaluated by experts with detailed feedback
  • Final certifying exam on skills in TensorFlow, PyTorch, and MLOps

Learning method

  • Active pedagogy with 70% hands-on practice on real business cases
  • Individualized support in small groups of max 10 participants
  • Open-source TensorFlow and PyTorch tools for immediate exercises
  • Unlimited access to post-training resources for review

Methods, materials and delivery

The Training Model Distillation - Compress ML Models for Production 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 ML Models for Production 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 ML Models for Production 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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