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 LLMs in Production training are: Advanced proficiency in Python, REST/GraphQL APIs, Docker/Kubernetes, and OpenAI/Anthropic API usage. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LiteLLM - Orchestrating Multi-Provider LLMs in Production 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 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 LiteLLM - Orchestrating Multi-Provider 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 LiteLLM - Orchestrating Multi-Provider 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 objectives

Master LiteLLM to unify LLM calls in a professional environment

Develop scalable multi-provider proxies with load balancing

Implement comprehensive observability to monitor costs and performance

Optimize security and LLM budgets in certified enterprise settings

Deploy LiteLLM in production Kubernetes with advanced virtual keys

Design robust LLM pipelines for critical applications

Acquire expert skills in intelligent routing and caching

Training program

Module 1litellm: expert installation and configuration (docker, helm, virtual keys)

Dive into advanced LiteLLM installation via Docker Compose and Helm for Kubernetes, configure custom virtual keys for fine-grained multi-team access management, integrate test environments with real providers like OpenAI and Anthropic, perform practical exercises on migrating an existing proxy, produce a production-ready setup with secrets management via Vault, and validate through a secure configuration audit that immediately enhances your LLM deployments.

Module 2litellm: advanced routing and load balancing (multi-providers, fallbacks)

Explore LiteLLM's intelligent routing to dispatch requests to OpenAI, Grok, Claude with dynamic priorities, implement weighted load balancing and automatic fallbacks in case of downtime, test on real enterprise cases with traffic spikes, develop custom rules via YAML and Python callbacks, generate routing reports to optimize costs, and deploy a unified proxy that reduces latencies by 40% in your critical AI applications.

Module 3litellm: expert observability (logging, metrics, otel tracing)

Install Prometheus, Grafana, and OpenTelemetry in LiteLLM for real-time monitoring of consumed tokens, provider latencies, and errors, configure customized dashboards for budget alerts, analyze structured logs with ELK stack on massive workloads, practice trace correlation for complex debugging, produce deliverables like LLM SLOs and weekly reports, transforming your data into actionable insights that save thousands of euros on hidden overcosts.

Module 4litellm: security and cost optimization (rate limits, caching, budgets)

Strengthen LiteLLM with distributed Redis rate limiting, semantic caching for recurrent responses, and budgets per virtual key to control spending by team, implement JWT auth and WAF rules against LLM injections, test abuse scenarios on exposed APIs, optimize prompts via automatic compression, generate cost forecasts based on history, and deploy a system that halves your provider bills while boosting the resilience of your professional AI services.

Module 5litellm: production deployment and scaling (k8s, ci/cd, advanced cases)

Master horizontal scaling of LiteLLM on Kubernetes with HPA and autoscaling metrics, integrate GitHub Actions for automated CI/CD of configs, explore expert cases like multi-tenancy and hybrid cloud providers, complete a full red thread project for an enterprise LLM API, perform stress tests with Locust for 10k req/s, produce custom Helm charts and ops docs, and conclude with a peer review that prepares your teams for zero-downtime deployments in real production.

Evaluation method

Expert multiple-choice quiz to validate learning outcomes at the end of the training
Continuous assessment through practical exercises and code reviews
Presentation of the LiteLLM red thread project in production conditions