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 Training LanceDB - Mastering Vector Search for AI training?

The Training LanceDB - Mastering Vector Search for AI training takes place over 5 journées. 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 Training LanceDB - Mastering Vector Search for AI training are: Advanced Python mastery, vector embeddings (Hugging Face), NoSQL databases and ML. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LanceDB - Mastering Vector Search for AI training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training LanceDB - Mastering Vector Search for AI | 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 - Mastering Vector Search for AI 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 - Mastering Vector Search for AI 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 - Mastering Vector Search for AI 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 LanceDB architecture for professional vector databases

Implement optimized vector indexes with LanceDB in enterprise settings

Develop scalable RAG pipelines integrating LanceDB and LLMs

Optimize semantic search performance with LanceDB

Manage multimodal data in LanceDB for generative AI

Deploy LanceDB in production on cloud or on-premise with certification

Acquire skills in monitoring and scaling LanceDB

Training program

Module 1LanceDB Architecture: Installation and Vector Embeddings (Python SDK, LanceDB Core)

Dive into quick LanceDB installation via pip, set up your first professional development environment, generate embeddings with Sentence Transformers and Hugging Face, insert millions of vectors in batch to simulate real enterprise cases, test initial KNN and range search queries, produce a performance visualization dashboard to validate your practical skills from day one.

Module 2Advanced LanceDB Indexing: IVF-PQ and Optimization (ANN-tree, Scalar Quantization)

Build ultra-performant IVF-PQ indexes on massive datasets, configure ANN-tree to accelerate hybrid searches, apply scalar quantization to reduce memory by 75% without loss of precision, conduct comparative benchmarks with FAISS and Pinecone on your own enterprise data, generate optimization reports, deploy a basic RAG prototype connected to LanceDB for smooth semantic queries.

Module 3RAG Pipelines with LanceDB: LLM Integration and Reranking (LangChain, LlamaIndex)

Integrate LanceDB into complete RAG pipelines with LangChain and OpenAI GPT, implement cross-encoder reranking to boost result relevance by 40%, manage dynamic chunking of PDF and text documents, test live on concrete cases like enterprise search or internal Q&A, produce a functional MVP with latency monitoring, optimize for production-scale volumes with certification.

Module 4Multimodal Data in LanceDB: Images, Audio and Fusion (CLIP, Whisper Embeddings)

Handle multimodal embeddings with CLIP for images and Whisper for audio in LanceDB, fuse text-image vectors for advanced cross-modal searches, process real enterprise multimedia datasets, implement metadata filters to refine results, benchmark performance on standard hardware, deliver a deployment-ready multimodal thread project with optimized code and professional documentation.

Module 5LanceDB Deployment and Scaling: Cloud, Monitoring (Docker, Kubernetes, Prometheus)

Containerize LanceDB with Docker for seamless horizontal scaling, deploy on Kubernetes or AWS S3 for cloud persistence, integrate Prometheus and Grafana for real-time QPS and latency monitoring, simulate 10x traffic spikes with load testing, secure with API keys and RBAC, finalize your certifiable thread project, obtain an enterprise migration plan and post-training support for immediate production rollout.