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.

What is the duration of the LangChain Expert Training - Deploy Scalable AI Apps training?

The LangChain Expert Training - Deploy Scalable AI Apps training takes place over 28 hours. The program is structured in progressive modules for optimal skill development.

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 LangChain Expert Training - Deploy Scalable AI Apps training are: Advanced mastery of Python, Experience with OpenAI API or Hugging Face, Solid knowledge in embeddings and prompt engineering.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the LangChain Expert Training - Deploy Scalable AI Apps training is available in remote. The format can be adapted to your needs (inter, intra, custom).

LangChain Expert Training - Deploy Scalable AI Apps | 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 LangChain Expert Training - Deploy Scalable AI Apps 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 LangChain Expert Training - Deploy Scalable AI Apps 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 LangChain Expert Training - Deploy Scalable AI Apps 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 access terms and deadlines

Training objectives

Build complex chains with LCEL and custom tools

Develop intelligent and reactive autonomous agents

Implement advanced RAG for precise and contextualized responses

Optimize memory and callbacks for scalable performance

Deploy LangChain applications in cloud production

Debug and monitor AI pipelines in real-time

Training program

Module 1Advanced LangChain Chains: LCEL, Parsers and Tools (Practical Exercises)

Dive into LCEL to assemble fluid chains, handle JSON and Pydantic parsers for structured outputs, integrate ready-to-use tools like Wikipedia or calculators, complete exercises on real Q&A cases, produce your first production-ready functional pipelines, and test live with LLMs like GPT-4.

Module 2LangChain Agents: Creation, Custom Tools and Reasoning (Agent Deliverables)

Create ultra-performant ReAct and OpenAI Functions agents, develop custom tools via @tool for web scraping or data analysis, simulate multi-agent collaborative scenarios, refine reasoning with concrete task automation examples, generate deployable agents ready for immediate use, and measure their effectiveness in real conditions.

Module 3LangChain RAG and Memory: Optimized Retrieval, Embeddings (Case Studies)

Master RAG with vector stores like FAISS or Pinecone, integrate advanced embeddings for precise semantic retrieval, manage conversational and episodic memory for persistent interactions, apply to real business datasets like customer support, produce custom RAG chains, and evaluate accuracy with metrics like ROUGE.

Module 4LangChain Deployment and Scaling: Monitoring, Best Practices (Final Project)

Deploy on FastAPI, Streamlit or LangServe for scalable apps, implement callbacks for logging and tracing with LangSmith, optimize for production with async and batching, debug common errors via observability, complete a full-stack AI agent capstone project, and leave with a cloud-deployed portfolio.