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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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The Training LoRA Fine-Tuning 2026 - Optimizing Edge 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.
To ensure the quality of the Training LoRA Fine-Tuning 2026 - Optimizing Edge AI for IoT training, Learni provides the following 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.
The assessment of skills acquired during the Training LoRA Fine-Tuning 2026 - Optimizing Edge AI for IoT training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Discover the basics of LoRA fine-tuning 2026 through a practical immersion in Python and PyTorch. Set up your environment with Hugging Face Transformers to load pre-trained models adapted for IoT. Test on real sensor datasets such as industrial vibrations or LoRaWAN environmental data. Perform your first supervised fine-tunings with guided exercises. Analyze precision/recall metrics in real-time. Produce initial performance reports to validate professional skills.
Dive into deployments of LoRA fine-tuned models on IoT edge devices via Docker and Kubernetes. Integrate MQTT-LoRa protocols for real-time data streams. Optimize LoRA hyperparameters with tools like PEFT to reduce memory footprint by up to 90%. Simulate concrete Industry 4.0 cases on connected sensors. Deploy company-adapted CI/CD pipelines. Evaluate model robustness via stress tests. Generate deployable deliverables including secure APIs and monitoring dashboards for immediate ROI.
Target audience
IoT engineers, embedded data scientists, company ML developers seeking to upskill in optimized AI
Prerequisites
Solid foundations in Python, introductory machine learning, IoT protocols such as MQTT and LoRa
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