What is the price of the Training LanceDB - Optimizing AI Vector Search training?

The price of the Training LanceDB - Optimizing AI Vector Search training is 6125€ HT. This includes access to all modules, training materials and personalized support. Funding solutions are available through OPCO.

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 - Optimizing AI Vector Search training?

The Training LanceDB - Optimizing AI Vector Search 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 - Optimizing AI Vector Search training are: Mastery of Python, experience with embeddings and vector databases like Pinecone or FAISS. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training LanceDB - Optimizing AI Vector 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 Training LanceDB - Optimizing AI Vector 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 Training LanceDB - Optimizing AI Vector 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 Training LanceDB - Optimizing AI Vector 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 the advanced architecture of LanceDB for professional AI applications

Develop optimized and scalable vector indexes in enterprise settings

Implement high-performance RAG pipelines with LanceDB and LLMs

Configure hybrid queries for robust semantic search

Optimize LanceDB performance in production environments

Integrate LanceDB into cloud and on-premise stacks for enterprise skills

Deploy secure and maintainable vector solutions

Training program

Module 1LanceDB Architecture: Advanced Vector Indexing (embeddings, HNSW, IVF)

Dive into the internal mechanisms of LanceDB to create high-performance vector collections, generate and store embeddings with models like Sentence Transformers or OpenAI, configure HNSW and IVF indexes to accelerate ANN searches, perform practical exercises on real enterprise datasets, produce a first prototype of an optimized vector database with measured precision and latency metrics.

Module 2Advanced LanceDB Queries: Hybrid Search and Filtering (metadata, fused scores)

Master hybrid queries combining vectors and keywords in LanceDB, implement filters on metadata for precise business cases like e-commerce search, test re-ranking algorithms with Reciprocal Rank Fusion, apply this to real RAG scenarios through interactive exercises, generate performance reports showing gains in recall and speed, while integrating tools like LlamaIndex for immediate productivity.

Module 3RAG Pipelines with LanceDB: LLM Integration and AI Agents (LangChain, Haystack)

Build end-to-end Retrieval-Augmented Generation pipelines with LanceDB as the vector backend, connect to LLMs like GPT-4 or Llama via LangChain, develop autonomous AI agents for dynamic querying, simulate enterprise use cases like intelligent chatbots or document analysis, optimize chunking strategy and result hydration, deliver a functional continuous project with qualitative and quantitative evaluation of generated responses.

Module 4Scaling LanceDB: Distributed Clustering and Performance Tuning (sharding, caching)

Scale up with LanceDB in distributed mode, configure sharding to handle millions of vectors without speed loss, implement caching and batching for intensive workloads, benchmark on Kubernetes or AWS clusters, analyze bottlenecks with integrated profiling tools, apply best practices for high availability in production, produce monitoring dashboards proving scalability and ROI for your enterprise AI projects.

Module 5Production Deployment of LanceDB: Security, Monitoring, and CI/CD (Docker, Helm)

Deploy LanceDB in secure production with data encryption and RBAC, integrate into CI/CD pipelines via Docker and Helm charts, configure monitoring with Prometheus and Grafana for proactive alerting, test resilience against failures and automated backups, finalize the continuous project with live deployment and security audit, receive a ready-to-use enterprise starter kit including scripts and certified documentation.