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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 Tracing 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 Tracing 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 Tracing 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.
Installation and setup of LangSmith for enterprise projects. Configuration of API keys and collaborative projects. Exploration of advanced LLM traces. Hands-on practice with concrete debugging cases. Creation of the red thread project: complex LLM chain. Exercises on LangChain-LangSmith integration. Management of secure test environments. Analysis of initial performance metrics. Implementation of custom logging. (112 mots)
Implementation of distributed tracing for multi-step agents. Capture of LLM inputs/outputs in real time. Use of annotations for complex workflows. Practical exercises on business cases: optimized RAG. Analysis of latencies and token costs. Configuration of hubs for shared datasets. Red thread project: tracing a conversational agent. Hands-on with nested spans. Integration with external observability tools. Evaluation of recurring error patterns. (118 mots)
Advanced interactive debugging techniques in LangSmith. Creation and management of custom evaluation datasets. Running benchmarks on various LLM models. Hands-on professional exercises: prompt tuning. Analysis of scores and advanced visualizations. Optimization of chains via feedback loops. Red thread project: debugging a failing RAG pipeline. A/B testing on LLM configurations. Management of biases and hallucinations. Export of reports for enterprise audits. (109 mots)
Scalable deployment strategies with LangSmith in production. Real-time monitoring and alerts configuration. CI/CD integration for LLM pipelines. Security of traces and GDPR compliance. Exercises on concrete cases: monitoring enterprise AI apps. Completion of the red thread project with custom dashboard. Cost optimization via LangSmith insights. Best practices for AI DevOps teams. Project presentation and code review. Post-training maintenance resources. (104 mots)
Target audience
AI Developers, LLM Engineers and Data Engineers for professional skills development
Prerequisites
Mastery of Python, LangChain and Large Language Models (LLM)
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