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 - Mastering Vector Databases for AI training are: Python basics, notions of embeddings and elementary machine learning. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training LanceDB - Mastering Vector Databases fo... | 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 Databases 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 Databases 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 Databases 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 the fundamentals of LanceDB for professional vector databases

Develop skills in creating and managing LanceDB collections

Implement optimized vector indexes in certified training

Design semantic searches for enterprise applications

Integrate LanceDB with ML pipelines in a professional context

Optimize query performance for real-world AI projects

Deploy a LanceDB database connected to embedding models

Training program

Module 1LanceDB Fundamentals and Installation

Introduction to LanceDB. Advantages of open-source vector databases. Installation via pip. Creating your first LanceDB database. Managing collections. Inserting vector data. Hands-on with simple exercises. Python environment setup. Real enterprise cases. Ongoing project initiated: e-commerce products database. Understanding serverless architecture. Basic unit tests. Detailed course materials provided. 7 hours of intensive practice.

Module 2Indexing and Searching with LanceDB

Building LanceDB vector indexes. Choosing similarity metrics. KNN search and range queries. Hybrid scalar-vector filters. Hands-on with real datasets. Professional exercises: text-image similarity. LanceDB memory optimization. Integrating Hugging Face embeddings. Advanced ongoing project: semantic product search. Handling upsert and delete. Performance benchmarks. RAG use cases. 7 hours of theory-practice alternation. Enterprise skills reinforced.

Module 3Advanced Integrations and LanceDB Deployment

Connecting LanceDB to LangChain and LlamaIndex. Complete RAG pipelines. LanceDB cloud scalability. Docker deployment. Monitoring and performance tuning. Data access security. Finalized ongoing project: full-stack RAG app. Exercises on real enterprise cases. Production best practices. Project evaluation. Validation quiz. Oral presentation. Post-training resources. Certification preparation. 7 hours of synthesis and practical deployment.