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 LiteLLM - Unified Proxy to Integrate All LLMs training are: Python basics (syntax, library management) and knowledge of REST APIs. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LiteLLM - Unified Proxy to Integrate All LLMs training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training LiteLLM - Unified Proxy to Integrate All… | 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 LiteLLM - Unified Proxy to Integrate All LLMs 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 LiteLLM - Unified Proxy to Integrate All LLMs 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 LiteLLM - Unified Proxy to Integrate All LLMs 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 LiteLLM installation for rapid professional integrations

Develop applications compatible with all LLM providers

Implement fallbacks and caching in LiteLLM for proven resilience

Configure a LiteLLM server proxy tailored to enterprise needs

Optimize LLM call costs and performance through hands-on training

Integrate LiteLLM into AI workflows to enhance skills

Produce unified LLM scripts ready for professional projects

Training program

Module 1LiteLLM Fundamentals: Installation and First Calls (Python, YAML config, providers)

Quick installation of LiteLLM via pip in a dedicated Python virtual environment, intuitive YAML file configuration to connect OpenAI, Anthropic, and Hugging Face, first synchronous and asynchronous calls to GPT-4o and Claude-3, practical exercises to create a basic chat interface, debugging common errors with integrated logging, production of a functional script tested on real enterprise cases, immediately enhancing your unified AI skills.

Module 2LiteLLM Integrations: Multi-Providers and Advanced Mechanisms (fallbacks, caching, streaming)

Exploration of LiteLLM multi-providers for seamless switching between models without recoding, implementation of automatic fallbacks in case of service unavailability, activation of Redis caching to speed up recurrent responses, streaming management for smooth real-time interactions, hands-on workshops on enterprise scenarios like a virtual assistant, resilience testing with simulated overload, creation of reusable modules to boost the efficiency of your professional AI projects.

Module 3LiteLLM Deployment: Server Proxy and Enterprise Optimization (Docker, monitoring, security)

Launching a LiteLLM proxy in server mode with Docker for scalable deployment, configuration of API key authentication and rate limiting for enterprise security, monitoring of costs and metrics via integrated observability, prompt optimization and batching to reduce LLM bills by 30%, red thread project on a complete RAG app, code review by the expert trainer, production-ready deliverables for certified deployment, transforming your skills into immediate competitive advantages.

Evaluation method

Multiple-choice quiz to validate knowledge on LiteLLM at the end of the training
Continuous assessment through practical LLM integration exercises
Presentation of the red thread unified proxy project to the trainer