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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 LiteLLM - Integrating Multi-Provider 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.
To ensure the quality of the Training LiteLLM - Integrating Multi-Provider LLMs 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 LiteLLM - Integrating Multi-Provider LLMs 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.
Quick discovery of LiteLLM via local and Docker installation, creation of an OpenAI-compatible proxy server, first calls to OpenAI, Anthropic, and open-source models like Llama, practical exercises to integrate LiteLLM into a simple Python script, multi-provider compatibility tests on real enterprise cases, production of a functional prototype with basic logging to validate professional skills.
Configuration of intelligent routing with automatic fallbacks and load balancing between providers, implementation of observability via Prometheus and tracing, exercises on cost management with per-model budgets, secure deployment of LiteLLM on cloud with Docker Compose, simulation of enterprise scenarios like scalable chatbots, completion of a deployable capstone project with monitoring, provision of production-ready configurations for certification.
Target audience
Python developers, AI engineers, software architects for LLM skill enhancement
Prerequisites
Python basics, knowledge of REST APIs and LLMs such as GPT or Llama
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