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.
Learning objectives
- Master LoRA fine-tuning for lightweight AI models in professional training
- Develop skills in optimizing model parameters for certified IoT applications
- Design fine-tuning pipelines adapted to enterprise edge computing constraints
- Implement LoRA integration with LoRa and MQTT sensor protocols
- Deploy optimized AI solutions on connected IoT devices
- Optimize energy performance of fine-tuned models in production
- Evaluate the business impact of LoRA fine-tuning in professional projects
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.
Program
Module 1LoRA Fine-Tuning Fundamentals: Advanced Architectures and PEFT Tools (PyTorch, Hugging Face)
Dive into the advanced mechanisms of LoRA fine-tuning for 2026, explore low-rank matrix decomposition, set up PyTorch and Hugging Face environments, perform practical exercises adapting lightweight LLM models, analyze the impact of parameterization on IoT accuracy, produce comparative reports on full fine-tuning vs. LoRA, and integrate real connected sensor cases to validate professional skills.
Module 2LoRA Fine-Tuning Implementation: IoT Datasets Pipelines (Sensors, MQTT, LoRa)
Build end-to-end LoRA pipelines with real IoT datasets, preprocess sensor data using Pandas and NumPy, fine-tune BERT-like models under edge memory constraints, test MQTT and LoRaWAN integration in simulations, develop automated scripts with Hugging Face Trainer, evaluate BLEU and perplexity metrics on embedded scenarios, and deliver functional prototypes ready for enterprise deployment.
Module 3LoRA Fine-Tuning Optimization: Quantization and Pruning for IoT 2026 (TensorRT, ONNX)
Optimize LoRA fine-tuned models using 8-bit quantization and structured pruning, convert to ONNX and TensorRT for edge deployment, simulate low-power LoRa constraints, benchmark latency and energy on real sensors, fine-tune advanced QLoRA hyperparameters, integrate real-time MQTT feedback loops, produce deployable IoT firmware deliverables, and analyze professional performance ROI.
Module 4Production Deployment of LoRA Fine-Tuning: Secure Scaling for IoT (Kubernetes, Docker)
Deploy LoRA fine-tuning solutions on Kubernetes and Docker IoT clusters, secure MQTT and LoRaWAN APIs with encryption, horizontally scale sensor fleets, monitor model drift with Prometheus and Grafana, migrate legacy embedded AI systems, conduct GDPR and Qualiopi compliance audits, finalize certifying portfolio projects, and prepare for post-training enterprise support.
Evaluation method
- Daily technical quizzes on LoRA fine-tuning for IoT
- Final certifying project: edge model deployment
- Evaluated real-world case studies on LoRa and MQTT sensors
Learning method
- Practical projects accounting for 70% of time on LoRA for IoT
- Anonymized company cases reflecting 2026 trends
- Pair exercises with personalized coach feedback
- Lifetime support and resources post-training
Methods, materials and delivery
The Training LoRA Fine-Tuning 2026 - Optimizing Embedded AI for IoT 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 2026 - Optimizing Embedded AI 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 LoRA Fine-Tuning 2026 - Optimizing Embedded AI 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
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
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
★★★★★
« cool, j'ai appris des trucs »
★★★★★
« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »
★★★★★
« la formation dev etait intense mais grave bien. merci Anthony Khelil »
★★★★★
« 😊👍 »
★★★★★
« bien 👍 »
★★★★★
« Allan Busi t'es au top, continue comme ça. formation géniale »
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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