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 Vision-Language Models - Mastering Multimodal AI 2026 training are: Python basics, machine learning fundamentals, and image manipulation. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Vision-Language Models - Mastering Multimodal AI 2026 training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Vision-Language Models - Mastering Mult... | 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 Vision-Language Models - Mastering Multimodal AI 2026 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 Vision-Language Models - Mastering Multimodal AI 2026 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 Vision-Language Models - Mastering Multimodal AI 2026 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 vision-language models for professional projects

Develop certified skills in multimodal image-text processing

Implement 2026 VLMs in real enterprise applications

Design AI pipelines combining vision and natural language

Optimize VLMs for production performance

Evaluate and deploy VLM solutions tailored to business needs

Training program

Module 1Vision-Language Models Fundamentals: Architecture and Principles (Python, Hugging Face)

Immersive discovery of vision-language models through installation of Python environments and Hugging Face Transformers, exploration of CLIP and BLIP architectures for associating images and text, practical exercises on multimodal datasets like COCO, creation of first image-text classification scripts, analysis of real business cases with immediate trainer feedback to solidify the basics.

Module 2Implementing Vision-Language Models: Fine-Tuning and Inference (Torch, Custom Datasets)

Hands-on fine-tuning of 2026 vision-language models with PyTorch and company datasets, configuration of pipelines for automatic caption generation on your images, exercises on zero-shot and few-shot tasks, real-time inference testing with tools like Gradio, production of functional deliverables ready to integrate into your AI workflows, focus on memory optimization for scalability.

Module 3Advanced Vision-Language Models Applications: RAG and Multimodal Agents (LangChain, APIs)

Building hybrid applications with vision-language models using LangChain and Retrieval-Augmented Generation for semantic visual search, integration of OpenAI and LLaVA APIs, collaborative workshops on business cases like e-commerce and document analysis, development of autonomous multimodal AI agents, accuracy evaluation with F1-score metrics, generation of personalized reports demonstrating immediate added value.

Module 4Deploying and Optimizing Vision-Language Models: Production and Security (Docker, Monitoring)

Mastery of deploying vision-language models in Docker and Kubernetes containers for enterprise environments, performance optimization with quantization and ONNX, implementation of monitoring with Prometheus, exercises on securing models against adversarial attacks, finalization of the red thread project with live deployment, delivery of optimized source code and action plan for rapid integration into your systems.