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 Weaviate Training - Implementing AI Semantic Search training are: Proficiency in Python, basic knowledge of NoSQL databases and vector embeddings. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Weaviate Training - Implementing AI Semantic Search training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Weaviate Training - Implementing AI Semantic Search | 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 Weaviate Training - Implementing AI Semantic Search 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 Weaviate Training - Implementing AI Semantic Search 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 Weaviate Training - Implementing AI Semantic Search 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

Master Weaviate for storing and querying professional vector data

Develop optimized data schemas as part of certified Weaviate training

Implement hybrid semantic search with enterprise-level skills

Integrate Weaviate into ML pipelines for scalable AI applications

Configure Weaviate modules for retrieval-augmented generation and RAG

Deploy Weaviate in production with monitoring and security

Optimize Weaviate performance for concrete business use cases

Training program

Module 1Weaviate Fundamentals: Installation and Vector Architecture (Docker, GraphQL)

Quick installation of Weaviate via Docker and setup of a local cluster, exploration of GraphQL and REST APIs for intuitive querying, initial exercises on creating classes and inserting vector data with OpenAI embeddings, setup of a flagship semantic search application project, practical tests to validate the professional environment and identify common pitfalls.

Module 2Weaviate Schema: Data Modeling and Vectorization (Hugging Face, schema design)

Design of complex schemas with vector properties and cross-references, generation of embeddings using integrated Hugging Face and Sentence Transformers modules, exercises on bulk importing real datasets like Wikipedia or e-commerce, data validation with Weaviate debugging tools, creation of custom views for business analytics, while building the flagship project with concrete enterprise cases.

Module 3Weaviate Search: Hybrid Semantic Queries (nearText, BM25, hybrid search)

Mastery of nearText and vector queries for precise semantic search, hybrid combination with BM25 for optimal results, practical exercises on score tuning and advanced filtering, implementation of personalized recommendations on the flagship dataset, performance analysis with recall/precision metrics, development of interactive test interfaces to visualize vector similarities in real-time.

Module 4Weaviate Modules: RAG and AI Generation (Q&A, transformers, OpenAI)

Activation of generative modules for RAG with LLMs like GPT or Llama, configuration of Q&A and summarization pipelines on vector data, integration with LangChain for advanced AI agents, exercises on semantic chatbots with retrieval context, prompt optimization and module chaining, application to the flagship project to demonstrate immediate business value in augmented search.

Module 5Weaviate Deployment: Scaling, Security, and Monitoring (Kubernetes, backups)

Production deployment on Kubernetes with Helm charts and horizontal scaling, API securing with authentication and multi-tenant tenancy, setup of automated backups and monitoring via Prometheus/Grafana, performance optimization for millions of vectors, completion of the flagship project with GitHub Actions CI/CD, code review, and maintenance plan for sustainable enterprise use.