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 - Storing High-Performance AI Embeddings training are: Python basics, knowledge of vector embeddings and machine learning. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LanceDB - Storing High-Performance AI Embeddings training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training LanceDB - Storing High-Performance AI E... | 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 - Storing High-Performance AI Embeddings 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 - Storing High-Performance AI Embeddings 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 - Storing High-Performance AI Embeddings 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 fundamentals of LanceDB for professional AI applications

Develop certifiable skills in vector storage and search

Configure LanceDB in a secure enterprise environment

Implement vector indexes for fast searches

Integrate LanceDB with Python for high-performance RAG pipelines

Optimize LanceDB collections for scalable professional projects

Produce vector prototypes ready for enterprise deployment

Training program

Module 1LanceDB Fundamentals: Installation and First Embeddings (Python, LanceDB CLI, vector datasets)

Immersive discovery of LanceDB basics through quick installation with pip and creation of a local database, hands-on with the CLI to import your first embeddings generated by Sentence Transformers, practical exercises on real image and text datasets, building a simple collection with associated metadata, initial similarity vector tests to validate learning, and generation of a basic performance report to export for your enterprise project.

Module 2Managing LanceDB Collections: Advanced Creation and Manipulation (Python API, Schemas, Upsert)

Deep dive into creating optimized LanceDB collections with custom schema definitions for high-dimensional vectors, using the Python API for massive upserts of millions of embeddings without speed loss, exercises on merging multimodal datasets like text and audio, live CRUD operations with metadata-filtered queries, concrete case of an e-commerce product database, production of a reusable script for enterprise automations, and collaborative code review.

Module 3Vector Searches in LanceDB: Indexing and ANN Queries (IVF-PQ, Hybrid Filtering)

Mastery of ANN indexes in LanceDB for sub-second searches on billions of vectors, IVF-PQ configuration to balance accuracy and speed, intensive practice of KNN queries with cosine or Euclidean similarity thresholds, integration of hybrid filters on metadata for advanced RAG cases, exercises on benchmark datasets like LAION, real-time measurement of recall and latency, development of a functional and optimized recommendation engine prototype ready for production deployment.

Module 4LanceDB Integration: Python Frameworks and RAG Pipelines (LangChain, LlamaIndex, FastAPI)

Seamless connection of LanceDB to popular Python ecosystems like LangChain for complete RAG chains, implementation of an end-to-end pipeline with OpenAI embedding generation and augmented retrieval, exercises on FastAPI servers exposing secure vector search endpoints, enterprise use case like internal chatbot on proprietary documents, live debugging of performance in simulated clusters, creation of a deployable RAG MVP with Docker, and Git code sharing for professional portfolio.

Module 5Optimization and Deployment of LanceDB: Enterprise Scalability (Cloud, Monitoring, Security)

Finalization with advanced optimization of LanceDB for AWS or GCP cloud environments, index tuning for high-concurrency workloads, setting up monitoring with Prometheus for query traceability, securing access via API keys and encryption of sensitive vectors, horizontal scaling exercises on Docker Compose clusters, incident simulation and quick resolutions, production of a migration plan from traditional databases, and defense of the thread project certifying your skills for an impactful CV.