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.
1 seat held for the next session
30:00Held for another
Request a call back and an advisor reaches you within 24 business hours, Monday to Saturday.
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning objectives
- Master the fundamentals of QLoRA through certified professional training
- Develop skills in quantized fine-tuning for IoT models
- Design effective LoRA adaptations tailored to connected sensors
- Implement QLoRA with MQTT and LoRa for optimized data transmission
- Optimize AI performance on edge devices in a corporate setting
- Deploy scalable QLoRA solutions for professional IoT projects
Our social commitment
A school kit donated to a child for every training
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
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.
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.
Program
Module 1QLoRA Fundamentals: LoRA Theory and Quantization (Python, Hugging Face)
Discover the principles of QLoRA through interactive theoretical modules, install essential Python tools like Transformers and PEFT, perform your first exercises loading pre-trained models, apply simple 4-bit quantization to a basic LLM, produce an initial report on memory savings achieved, while linking these concepts to IoT constraints such as low power consumption.
Module 2QLoRA Fine-Tuning: Adapting Models for IoT Sensors (Datasets, Training Loops)
Dive into practical fine-tuning with simulated IoT sensor datasets, configure optimized training loops using Accelerate, test adaptations on lightweight models like Phi-2, integrate loss and perplexity metrics, generate custom checkpoints, analyze impact on latency for edge devices, and prepare a fine-tuned model deliverable ready for corporate deployment.
Module 3QLoRA IoT Integration: MQTT and LoRa Protocols (Mosquitto, ChirpStack)
Integrate QLoRA with IoT protocols via MQTT broker Mosquitto and LoRaWAN with ChirpStack, develop Python scripts for edge inference, simulate real-time sensor data streams, optimize bandwidth with quantized models, test robustness in disconnected scenarios, produce an end-to-end pipeline prototype, and evaluate energy savings for industrial applications.
Module 4Advanced QLoRA Optimization: Sensors and Edge Computing (TensorRT, ONNX)
Optimize your QLoRA models for sensors via ONNX export and TensorRT conversion, calibrate for IoT hardware like Raspberry Pi, measure FPS and CPU/GPU consumption, apply complementary pruning techniques, deploy on edge simulators, generate comparative benchmarks, and design a monitoring dashboard to track production performance in a corporate environment.
Module 5QLoRA Deployment and Security: IoT Scalability (Docker, Mini-Kubernetes)
Deploy your QLoRA solutions in IoT-adapted Docker containers, configure lightweight Kubernetes orchestration for sensor fleets, integrate inference encryption with TLS, test scalability on 10+ virtual devices, draft a secure production rollout plan, produce a complete certifying project portfolio, and plan evolution toward professional corporate deployments.
Evaluation method
- Daily interactive quizzes on remote platforms
- Final QLoRA IoT project with detailed technical report
- Qualiopi certification attesting to acquired skills and deliverables
Learning method
- 70% hands-on practice with remote exercises
- 30% theory via videos and interactive live demos
- Dedicated Slack support for real-time questions
- Unlimited access to replays and post-training resources
Methods, materials and delivery
The Training QLoRA - Optimizing Lightweight AI Models for IoT 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 QLoRA - Optimizing Lightweight AI Models for IoT 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 QLoRA - Optimizing Lightweight AI Models for IoT 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.
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.
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.
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.
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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FAQ
Frequently asked questions
How much does the Training QLoRA - Optimizing Lightweight AI Models for IoT training cost?+
The individual price is $5,775 (USD). The team / on-site group package is shown on the course page. A detailed quote is sent within one business day.
How long is the Training QLoRA - Optimizing Lightweight AI Models for IoT training?+
The training lasts 5 journées, available live online (US time zones) or on-site at your offices.
How is this training paid for?+
Most US teams pay directly through their company (L&D or training budget). We invoice in US dollars and accept bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
Are there any prerequisites?+
Python basics, machine learning fundamentals, and elementary Linux environment
Is a certificate delivered at the end?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report.
Does Learni provide the equipment?+
No. A computer and stable internet connection are required for the participant. Learni provides the educational platform, the trainer and all course materials.
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