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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 LangSmith - 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 LangSmith - 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 LangSmith - Optimizing LLM Pipelines in Production training is carried out through:
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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.
Deep dive into advanced LangSmith installation, configuration of detailed traces to capture real-time LLM interactions, creation of custom datasets from real logs, practical exercises on debugging hallucinations and latencies, implementation of automated evaluations with precise metrics, analysis of real enterprise cases to identify bottlenecks, generation of actionable diagnostic reports.
Deployment of red thread projects with LangSmith integration for multi-step agents, API cost optimization via granular monitoring, setup of human and automated feedback loops, exercises on fine-tuning guided by LangSmith traces, production load simulations to test robustness, configuration of custom alerts and dashboards, finalization of an enterprise-ready LLM pipeline with complete documentation and scaling plan.
Target audience
AI Developers, ML Engineers, and LLM Architects for professional skill enhancement
Prerequisites
Mastery of LangChain, advanced Python, and production LLM application development
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