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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 NVIDIA NeMo Guardrails - Securing LLMs in Production 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 NVIDIA NeMo Guardrails - Securing LLMs in Production 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 NVIDIA NeMo Guardrails - Securing LLMs in Production 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.
Quick installation of NVIDIA NeMo Guardrails via pip and Docker, creation of your first secured project with default rails, exploration of key concepts like colls and flows, practical exercises on malicious prompts to test built-in protections, generation of initial audit reports and setup of a professional development environment, applying real business cases to validate learning from the first session.
Design of custom rails in YAML to block sensitive content, implementation of input checks with regex filters and embedded NLP models, development of conditional flows for dynamic responses, practical workshops on real scenarios such as jailbreak or bias detection, performance optimization with NVIDIA tools like CUDA, production of tested and documented deliverables for immediate team integration.
Seamless connection of NeMo Guardrails to popular LLMs via REST APIs or LangChain, implementation of end-to-end pipelines with advanced state management, exercises on enterprise use cases like secure chatbots or virtual assistants, addition of logging and monitoring with Prometheus, load testing for production simulation, creation of a functional ongoing project ready for deployment with complete security metrics.
Scalable deployment on Kubernetes with NVIDIA Helm charts, CI/CD setup for automated rail updates, performance optimization for low-latency inference, workshops on advanced security audits and false positive management, setup of a custom monitoring dashboard, finalization of the ongoing project with an enterprise maintenance plan, delivery of all artifacts for immediate and certified adoption.
Target audience
AI Developers, ML Engineers, and Data Scientists aiming to advance their skills in LLM security
Prerequisites
Proficiency in Python, experience with an LLM framework (such as Hugging Face Transformers), and fundamentals of computer security
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