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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 - Optimizing LLM Pipelines 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.
To ensure the quality of the Training LiteLLM - Optimizing LLM Pipelines in Production 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 - Optimizing LLM Pipelines in Production 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.
Dive into advanced LiteLLM installation on Docker, configure dozens of providers like OpenAI, Anthropic, Grok in a single unified YAML file, test hybrid calls with automatic fallbacks, perform practical exercises on an enterprise RAG use case, generate detailed logs and a first observability dashboard to validate your professional skills from the first day.
Design dynamic LiteLLM routes based on costs, latency, or availability, implement load balancing on Kubernetes with provided Helm charts, simulate traffic spikes on real enterprise datasets, optimize with integrated Redis caching, produce a resilient LiteLLM proxy tested under load via Locust, and document your rules for immediate reuse in production.
Integrate LiteLLM monitoring with Prometheus and Grafana to track API costs in real-time, deploy distributed tracing via OpenTelemetry on complex LLM flows, configure rate limiting and virtual keys to secure multi-team access, analyze metrics on concrete token leak cases, generate automated Slack alerts, and export reports for GDPR compliance audits in the enterprise.
Deploy LiteLLM serverless on AWS Lambda or Vercel with GitHub Actions CI/CD, optimize for 2026 features like multimodal streaming, test high availability with chaos engineering, measure ROI via 40% cost reduction on real workloads, finalize a scalable red thread project deliverable, and prepare for skills certification to boost your professional AI career.
Target audience
AI Engineers, backend developers, and cloud architects seeking to advance their professional LLM skills
Prerequisites
Advanced Python proficiency, experience with LLM APIs (OpenAI, Anthropic), Docker and FastAPI
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