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Training LoRA Fine-Tuning - Optimize Low-Power IoT Networks

Ref: WYE109
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 LoRA fine-tuning techniques to optimize range and reliability in professional IoT
  • Configure LoRaWAN gateways with MQTT for seamless enterprise integration
  • Develop skills in deploying low-power LoRa sensors
  • Implement energy optimization strategies during the certification training
  • Design scalable LoRa networks for industrial projects
  • Evaluate and troubleshoot LoRa performance in real-world contexts
  • Acquire professional skills validated by Qualiopi certification

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 1LoRA Fine-Tuning Fundamentals: Protocols and Modulations (LoRaWAN, ChirpStack)

Discover advanced LoRA principles, adjust spreading factor and bandwidth parameters to maximize range, perform labs on SX1276 modules with Arduino, simulate IoT scenarios via The Things Network, analyze power consumption impacts using tools like LoRa Calculator, produce an initial configuration report, integrate MQTT for first data stream, consolidate with pair practical exercises.

Module 2Advanced LoRA Fine-Tuning: Gateways and MQTT Integration (TTN, Mosquitto)

Configure LoRaWAN gateways in-person, fine-tune EU868 frequencies to avoid interference, implement MQTT broker with Node-RED, test real-time sensor payload transmission, optimize QoS for industrial reliability, deploy multi-node network via ChirpStack, analyze logs with Grafana, generate monitoring dashboards, apply to connected factory use case, validate via supervised deployment.

Module 3LoRa Sensors and Energy Fine-Tuning: Sensors & Duty Cycle (BME280, Optimization)

Integrate temperature/humidity sensors on LoRa nodes, fine-tune duty cycle to comply with ETSI regulations, measure battery life via long-duration tests, program with MicroPython on ESP32-LoRa, optimize ADR for dynamic adaptation, simulate agritech scenarios with 50+ nodes, integrate MQTT for alerting, produce optimized firmware, test in controlled environment, report quantified energy savings.

Module 4LoRA Fine-Tuning Security and Scalability: Encryption & Mesh (ABPS, LoRaMesh)

Strengthen LoRaWAN security with ABP/OTAA keys, fine-tune sessions for anti-replay, deploy hybrid mesh networks, integrate secure TLS MQTT, scale to 100 nodes via Semtech simulations, manage inter-gateway roaming, analyze vulnerabilities with Wireshark, apply to smart city, optimize latency under load, generate enterprise security policy, validate via guided practical audit.

Module 5LoRA Fine-Tuning Deployment & Maintenance: Real Cases & Troubleshooting (Production Tools)

Deploy complete LoRa network in real conditions, fine-tune performance via field tests, integrate production MQTT dashboard, train on SNR/RSSI troubleshooting, optimize via predictive machine learning, 4.0 factory case study with vibration sensors, migrate legacy to LoRaWAN 1.1, produce maintenance plan, evaluate quantified ROI, conclude with certifying group project, obtain actionable feedback.

Evaluation method

  • Daily technical quizzes on LoRA fine-tuning
  • Final project: deployed and optimized IoT network
  • Practical evaluation by Qualiopi-certified trainer

Learning method

  • Hands-on labs on real LoRa hardware (modules, gateways)
  • Industrial IoT case studies with LoRa/MQTT
  • Advanced simulations and parameterized fine-tuning
  • Pair work for collaborative projects

Methods, materials and delivery

The Training LoRA Fine-Tuning - Optimize Low-Power IoT Networks 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 LoRA Fine-Tuning - Optimize Low-Power IoT Networks 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 LoRA Fine-Tuning - Optimize Low-Power IoT Networks 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
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« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
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« 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
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« 😊👍 »

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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