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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 Vision-Language Models - Deploying Multimodal AI in the Enterprise 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 Vision-Language Models - Deploying Multimodal AI in the Enterprise 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 Vision-Language Models - Deploying Multimodal AI in the Enterprise training is carried out through:
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.
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.
Dive into the advanced foundations of vision-language models, install the Hugging Face Transformers and PyTorch environment, analyze CLIP and BLIP architectures on concrete datasets like COCO and Flickr30k, perform zero-shot image-text classification exercises, generate multimodal embeddings for semantic search, produce a first vision-language aligned prototype with personalized code review to boost your professional skills.
Get hands-on with supervised fine-tuning and LoRA on your enterprise datasets, use tools like Accelerate and PEFT to optimize GPU resources, train vision-language models on VQA and image captioning tasks, test on real e-commerce and medical cases, generate performance reports with BLEU and CLIP-score metrics, iterate on hyperparameters for production-ready results, and leverage these certified multimodal AI skills directly.
Build interactive applications with Gradio and Streamlit, integrate vision-language models into retrieval-augmented generation pipelines, develop multimodal chatbots responding to image+text queries, explore use cases in marketing and security such as visual anomaly detection, deploy real-time demos with iterative feedback, analyze business impact via concrete KPIs, and transform your ideas into deployable prototypes that impress in the enterprise.
Master containerized deployment with Docker and Kubernetes, convert vision-language models to ONNX for fast multi-platform inference, optimize latency and memory with quantization and distillation, secure APIs with FastAPI and authentication, monitor in production via Weights & Biases, complete a full capstone project on a real enterprise challenge, and obtain a certified deliverable ready for integration into your professional AI workflows.
Target audience
Data scientists, machine learning engineers, corporate AI managers seeking to advance in multimodal skills
Prerequisites
Proficiency in Python, PyTorch/TensorFlow, and deep learning in vision and NLP
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