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 - Orchestrating Multi-Provider LLM APIs training are: Advanced proficiency in async Python, LLM APIs (OpenAI, Anthropic), Docker, and cloud management. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LiteLLM - Orchestrating Multi-Provider LLM APIs training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training LiteLLM - Orchestrating Multi-Provider… | 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 - Orchestrating Multi-Provider LLM APIs 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 - Orchestrating Multi-Provider LLM APIs 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 - Orchestrating Multi-Provider LLM APIs 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 objectives

Master LiteLLM to unify LLM API calls in professional projects

Develop scalable LiteLLM proxies with intelligent multi-provider routing

Implement caching, retry, and fallbacks to optimize enterprise performance

Design production-grade LLM pipelines with advanced monitoring and logging

Deploy LiteLLM in production via Docker and Kubernetes for high availability

Optimize costs and performance of generative AI workflows in professional contexts

Training program

Module 1LiteLLM Fundamentals: Installation and Multi-Provider Configuration (Python, OpenAI, Anthropic)

Express installation of LiteLLM via pip in a professional virtual environment, API key configuration for OpenAI GPT, Anthropic Claude, and Azure OpenAI, initial synchronous and asynchronous calls to various LLM models, practical exercises on dynamic routing to the optimal provider based on cost and latency, creation of a basic proxy tested on real cases like automated text generation, production of structured logs for immediate analysis—all designed to rapidly enhance your skills in a continuous, hands-on flow.

Module 2Advanced LiteLLM Features: Caching, Retry, and Observability (Redis, Prometheus)

Implementation of Redis caching to accelerate repetitive LLM responses and reduce API costs by 40%, deployment of intelligent retry strategies with exponential backoff for provider errors, integration of automatic fallbacks to secondary models, development of custom instrumentation for tracing via OpenTelemetry, exercises on enterprise scenarios such as resilient chatbots and RAG systems, real-time observability dashboards, delivering immediate value for performant and reliable production AI applications.

Module 3LiteLLM Deployment: Scalability, Security, and Production Optimization (Docker, Kubernetes)

LiteLLM containerization with Docker Compose for fast, reproducible deployments, Kubernetes orchestration for horizontal auto-scaling under intensive LLM loads, endpoint securing with JWT authentication and rate limiting, fine-tuned cost optimization via virtual keys and per-tenant budgeting, advanced monitoring with Grafana and Slack alerts, finalization of the red-thread project on a complete enterprise workflow like a multi-model virtual assistant, delivery of ready-to-deploy configurations and maintenance plan, ensuring expert mastery for your enterprise AI challenges.