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 Training LanceDB - Implementing Scalable Vector Databases training are: Proficiency in Python, knowledge of vector embeddings and machine learning. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LanceDB - Implementing Scalable Vector Databases training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training LanceDB - Implementing Scalable Vector ... | 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 Training LanceDB - Implementing Scalable Vector Databases 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 Training LanceDB - Implementing Scalable Vector Databases 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 Training LanceDB - Implementing Scalable Vector Databases 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 objectives

Master the LanceDB architecture for professional AI applications

Develop performant and scalable vector indexes in enterprise settings

Implement production-ready RAG pipelines with LanceDB

Optimize semantic queries for advanced competencies

Integrate LanceDB with ML frameworks for real-world projects

Deploy optimized LanceDB solutions in production

Design vector databases tailored to professional needs

Training program

Module 1LanceDB Architecture: Advanced Configuration and Vector Modeling (Python SDK, CLI, HuggingFace Embeddings)

Installation and configuration of a professional LanceDB environment, creation of vector collections with advanced schemas, generation of embeddings via HuggingFace models, massive ingestion of structured and unstructured data, practical exercises on real enterprise datasets, validation of structures with unit tests to ensure immediate scalability.

Module 2LanceDB Indexing: Advanced Techniques for Optimal Performance (IVF-PQ Index, Metadata Filtering)

Exploration of LanceDB indexing algorithms like IVF-PQ to accelerate searches, fine-tuning of indexes with hybrid metadata, implementation of SQL-like filters for complex queries, comparative benchmarks on massive volumes, practical workshops to optimize a RAG use case, production of performance reports demonstrating up to 10x latency gains.

Module 3LanceDB Queries: RAG Implementation and Hybrid Search (LangChain Pipelines, Streaming)

Development of complete RAG pipelines integrating LanceDB with LangChain and LLMs, hybridization of vector and keyword search via BM25, streaming management for real-time responses, integration into full-stack Python/FastAPI applications, concrete cases on enterprise chatbots, creation of a functional prototype with qualitative evaluation of semantic results.

Module 4LanceDB Deployment: Scalability and Production Monitoring (Docker, Kubernetes, Observability)

Containerization of LanceDB with Docker for cloud-agnostic deployments, Kubernetes orchestration for scalable clusters, setup of monitoring with Prometheus and Grafana, resource optimization for reduced costs, high-traffic load simulations, finalization of a professional red thread project with maintenance plan, handover ready for the IT team.