Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training LoRA Fine-Tuning - Efficiently Adapt AI for IoT training are: Basic Python programming skills, elementary machine learning concepts, installed development environment. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LoRA Fine-Tuning - Efficiently Adapt AI for IoT training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training LoRA Fine-Tuning - Efficiently Adapt AI… | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training LoRA Fine-Tuning - Efficiently Adapt AI for IoT training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training LoRA Fine-Tuning - Efficiently Adapt AI for IoT training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training LoRA Fine-Tuning - Efficiently Adapt AI for IoT training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master the fundamentals of LoRA fine-tuning in certified professional training

Develop skills to adapt AI models to concrete IoT use cases

Design optimized fine-tuning pipelines for enterprise use

Implement LoRA on IoT sensor datasets using Hugging Face tools

Configure edge deployments for professional applications

Optimize model performance to reduce IoT energy costs

Acquire certification validating LoRA fine-tuning skills

Training program

Module 1LoRA Fine-Tuning Fundamentals: Key Concepts and Setup (Python, Hugging Face, IoT Datasets)

Discover the principles of LoRA fine-tuning adapted to IoT, install the environment with essential libraries like Transformers and PEFT, explore real sensor datasets, perform your first practical exercises on pre-trained models, analyze efficiency gains compared to full fine-tuning, produce an initial configuration report validated by the trainer.

Module 2LoRA Fine-Tuning Dataset Preparation: Cleaning and Augmentation (Pandas, Torchaudio for Sensors)

Dive into IoT-specific data preparation, clean raw sensor streams with Pandas and dedicated tools, apply augmentation techniques for MQTT-LoRa streams, test dataset quality via visualizations, integrate real industrial company cases, generate ready-to-use fine-tuning pipelines, evaluate impact on model accuracy in supervised exercises.

Module 3LoRA Fine-Tuning Implementation: Practical Training (Accelerate, Weights & Biases)

Launch your first LoRA trainings on BERT-like models for IoT tasks, configure optimal hyperparameters with Accelerate, monitor via Weights & Biases in real-time, apply to sensor scenarios and LoRa networks, optimize convergence on limited hardware, produce saved checkpoints, analyze metrics for rapid iterations in small groups.

Module 4LoRA Fine-Tuning Optimization: Quantization and Merging (BitsAndBytes, LoRAX)

Advance to optimization with 4-bit quantization via BitsAndBytes, merge LoRA adapters for IoT edge deployment, test on real LoRaWAN simulations, reduce latency by 50% in practical exercises, integrate cross-validation evaluation, prepare models for enterprise production, validate performance via concrete sensor benchmarks.

Module 5LoRA Fine-Tuning Deployment: IoT Integration and Certification (Docker, MQTT Broker)

Deploy your fine-tuned LoRA models in IoT environments via Docker and MQTT brokers, integrate live sensors for end-to-end tests, secure APIs for professional use, measure ROI in data cost reduction, finalize a personal capstone project, obtain Qualiopi certifying attestation, benefit from a post-training plan for lasting skills.