Training IoT Pruning and Distillation - Optimize Embedded Models

$5,775

  • 5 days
  • Remote
  • 10 people max.
Ref: BMO501
10 people max.
$5,775
In-company rate: from $10,395 for the whole team
On-site and certification exam on quote
5 days
Remote

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.

Célian Lebacle, Learni trainer · Web & mobile expert
Célian Lebacle

Learni trainer · Web & mobile expert

73%productivity gap
×3cost of inaction
Flag of the United StatesBuilt for US teams
Payment by wire transferEnhanced confidentialityPerfect for companies with 50 to 500 employeesScheduling to fit any time zone

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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft and Microsoft.

IoT Pruning and Distillation training objectives

  • Master structured network pruning to reduce latency and power consumption on IoT devices without losing operational performance.
  • Distill a large model into a compact one suited for sensors and microcontrollers while measuring the actual residual gap.
  • Combine pruning, distillation, and quantization in the optimal order for memory- and energy-constrained embedded systems.
  • Compare results against a smaller model trained directly on IoT data to validate the value of the reduction.
  • Determine the critical threshold where compression begins to degrade the business task on embedded targets.
  • Deploy and benchmark optimized models on Arduino, Raspberry Pi, and FPGA with latency, RAM, and energy metrics.

Program

IoT Pruning and Distillation curriculum: 5 modules over 5 days

Module 1 : Structured Pruning for IoT Targets (TensorFlow, PyTorch)

Analysis of model architectures on embedded devices. Hands-on structured pruning with measurement of latency, memory, and energy consumption on Raspberry Pi and microcontrollers. Concrete sensor and local detection network cases. Deliverables: pruned and benchmarked models with impact report on IoT tasks.

Module 2 : Distillation to Compact Models (Knowledge Distillation)

Knowledge transfer from a teacher model to a lightweight student for edge computing. Exercises on sensor data with temperature and loss tuning. Performance comparison before/after on Arduino and FPGA. Deliverables: deployable student model with accuracy and latency metrics.

Module 3 : Quantization and Combined Pipeline (TensorRT, ONNX)

Sequential integration of pruning-distillation-quantization to minimize footprint on embedded systems. Tests on KNX and Home Assistant with LoRa sensors. Residual gap and consumption measurement. Deliverables: complete pipeline and quantized models ready for RTOS.

Module 4 : Comparison with Lightweight Native Models (TinyML, Edge Impulse)

Direct training of small models versus reduction pipeline on real IoT datasets. Analysis of thresholds where pruning loses value. Cross-benchmarks on FPGA and Raspberry Pi. Deliverables: comparison table and selection recommendations.

Module 5 : Decision and Final Deployment (Firmware, MQTT)

Selection of acceptable compression point based on IoT business constraints. Integration of optimized firmware with MQTT and RTOS. Full tests on sensors and home automation. Deliverables: final deployed model and operations documentation.

How IoT Pruning and Distillation skills are assessed

  • Latency and memory benchmarks on embedded targets
  • Final IoT model reduction project
  • Residual gap reports and technical decisions

How the IoT Pruning and Distillation course is taught

  • Intensive hands-on practice with real hardware
  • IoT and sensor use cases
  • Open-source tools and edge frameworks
  • Iterative feedback on deliverables

IoT Pruning and Distillation training: access and practical arrangements

The Training IoT Pruning and Distillation - Optimize Embedded Models program is delivered onsite or remote (blended-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 IoT Pruning and Distillation - Optimize Embedded Models 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 IoT Pruning and Distillation - Optimize Embedded Models 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

Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.

The Learni story

Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.

Our method

How Learni guarantees the quality of your IoT Pruning and Distillation training

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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Our social commitment

A school kit donated to a child for every training booked

To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.

  • Backpack, notebooks and essential supplies
  • Distributed through our partner charities
  • Included, at no extra cost to you
Enroll and offer a kit
FAQ

IoT Pruning and Distillation: frequently asked questions

Which tools are used in IoT Pruning and Distillation training?+
Hands-on work runs on tensorFlow, pyTorch, knowledge Distillation, tensorRT, ONNX and tinyML. Those tools come from the IoT Pruning and Distillation program itself, delivered remotely over 5 days, so participants practise on the stack they will use at work.
What can participants do after IoT Pruning and Distillation training?+
They can master structured network pruning to reduce latency and power consumption on IoT devices without losing operational performance, distill a large model into a compact one suited for sensors and microcontrollers while measuring the actual residual gap and combine pruning, distillation, and quantization in the optimal order for memory- and energy-constrained embedded systems. Those are the stated objectives of the IoT Pruning and Distillation program at Expert level, checked by the end-of-session assessment.
What does the IoT Pruning and Distillation program cover, day by day?+
Day 1: Structured Pruning for IoT Targets (TensorFlow, PyTorch). Day 2: Distillation to Compact Models (Knowledge Distillation). Day 3: Quantization and Combined Pipeline (TensorRT, ONNX). Day 4: Comparison with Lightweight Native Models (TinyML, Edge Impulse). Day 5: Decision and Final Deployment (Firmware, MQTT). The IoT Pruning and Distillation program runs over 5 days, delivered remotely.
How is IoT Pruning and Distillation training assessed?+
Assessment relies on latency and memory benchmarks on embedded targets, final IoT model reduction project and residual gap reports and technical decisions. Learni issues a completion certificate together with the individual assessment report at the end of the IoT Pruning and Distillation program, which an L&D team can file as evidence.
How much does IoT Pruning and Distillation training cost per participant?+
IoT Pruning and Distillation training costs $5,775 per participant. For an in-company team, the flat rate starts at $10,395 for the whole group, up to 8 people. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.
How long does IoT Pruning and Distillation training take?+
5 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for IoT Pruning and Distillation training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before IoT Pruning and Distillation training?+
Confirmed proficiency in Python, ML frameworks (TensorFlow or PyTorch), deployment on microcontrollers or Raspberry Pi, and solid foundations in neural networks.
Is a certificate issued after IoT Pruning and Distillation training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for IoT Pruning and Distillation training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
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