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 Guardrails.ai - Securing LLMs in Production training are: Advanced Python proficiency, experience with LLM APIs (OpenAI, Hugging Face), knowledge of Pydantic data validation. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Guardrails.ai - Securing LLMs in Production training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Guardrails.ai - Securing LLMs in Produc... | 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 Guardrails.ai - 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.

Teaching resources provided

To ensure the quality of the Training Guardrails.ai - Securing LLMs in Production 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 Guardrails.ai - Securing LLMs in Production 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 access terms and deadlines

Training objectives

Master Guardrails.ai for validating and correcting LLM outputs in a professional context

Develop custom validators tailored to company-specific business needs

Implement RAIL specifications for certified and robust AI pipelines

Design Guardrails.ai integrations with popular LLM frameworks

Optimize Guardrails.ai performance in production for scalable applications

Deploy monitoring and logging systems with Guardrails.ai

Acquire certified skills in AI guardrails for enterprise projects

Training program

Module 1Guardrails.ai Fundamentals: Installation and Basic Validators (Pydantic, Regex, Types)

Quick installation of Guardrails.ai in a professional Python environment, creation of the first validator to correct basic LLM outputs, exploration of native integrations with OpenAI and Hugging Face, practical exercises on real business prompts, development of validation rules for structured data, unit tests and error report generation, production of a first validated pipeline ready for the team.

Module 2Advanced Guardrails.ai: RAIL Specs and Custom Validators (Business Logic, XML/JSON)

Design of complete RAIL specifications for multimodal outputs, creation of custom validators with advanced Python for specific business cases, integration of Guardrails.ai into complex LLM chains via LangChain, practical workshops on correcting hallucinations and biases, simulation of business scenarios with sensitive data, performance optimization via caching and batching, delivery of a reusable documented module for your AI projects.

Module 3Guardrails.ai in Production: Monitoring, Deployment, and Scaling (Docker, Observability)

Deployment of Guardrails.ai in Docker containers for cloud environments, setup of advanced monitoring with logging and Prometheus metrics, real-time error management and fallback strategies, exercises on horizontal scaling for high loads, concrete GDPR-compliant audit cases, best practices for CI/CD with Guardrails.ai, finalization of a deployable red thread project with validation dashboard, skills ready to boost your enterprise AI applications.