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Training QLoRA - Optimizing Lightweight AI Models for IoT

Course created on July 27, 2026Updated on October 5, 2026

On quote$5,775

5 days
Onsite
10 people max.

Request a quote See the program

Ref: KII552

10 people max.

On quote$5,775

In-company rate: on quotefrom $10,395 for the whole team

On-site and certification exam on quote

5 days

Onsite

Don't let this gap widen

Why this program matters

Without QLoRA mastery, your IoT projects waste up to 80% of memory resources on edge devices, driving energy costs up by a factor of 5 according to Gartner 2023.
Unoptimized models cause critical latencies, with 40% deployment failures in industrial production.
Your company risks losing contracts to agile competitors, and your career stalls without certified skills in lightweight AI.
Invest now to turn these risks into lasting competitive advantages.

Trusted by — schools, companies and industrial groups

25,718 programs · 25 countries

Every program built with a dedicated engineer

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

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

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.

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

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.

A child walking to school with a backpack

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

Frequently asked questions

Which tools are used in QLoRA training?+

Hands-on work runs on python, hugging Face, datasets, training Loops, mosquitto and chirpStack. Those tools come from the QLoRA program itself, delivered on site over 5 days, so participants practise on the stack they will use at work.

What can participants do after QLoRA training?+

They can master the fundamentals of QLoRA through certified professional training, develop skills in quantized fine-tuning for IoT models and design effective LoRA adaptations tailored to connected sensors. Those are the stated objectives of the QLoRA program at Beginner level, checked by the end-of-session assessment.

What does the QLoRA program cover, day by day?+

Day 1: QLoRA Fundamentals: LoRA Theory and Quantization (Python, Hugging Face). Day 2: QLoRA Fine-Tuning: Adapting Models for IoT Sensors (Datasets, Training Loops). Day 3: QLoRA IoT Integration: MQTT and LoRa Protocols (Mosquitto, ChirpStack). Day 4: Advanced QLoRA Optimization: Sensors and Edge Computing (TensorRT, ONNX). Day 5: QLoRA Deployment and Security: IoT Scalability (Docker, Mini-Kubernetes). The QLoRA program runs over 5 days, delivered on site.

How is QLoRA training assessed?+

Assessment relies on daily interactive quizzes on remote platforms, final QLoRA IoT project with detailed technical report and qualiopi certification attesting to acquired skills and deliverables. Learni issues a completion certificate together with the individual assessment report at the end of the QLoRA program, which an L&D team can file as evidence.

How much does QLoRA training cost per participant?+

QLoRA training is priced on quote. The amount depends on headcount, format and how far the syllabus is tailored to your team. A detailed quote is sent within one business day, and team pricing covers the whole group from the second participant.

How long does QLoRA 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 QLoRA 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 QLoRA training?+

Python basics, machine learning fundamentals, and elementary Linux environment.

Is a certificate issued after QLoRA 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.

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