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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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The LanceDB Training - Deploying 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.
To ensure the quality of the LanceDB Training - Deploying Scalable Vector Databases training, Learni provides the following 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.
The assessment of skills acquired during the LanceDB Training - Deploying Scalable Vector Databases training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Dive into configuring an expert LanceDB environment, install the Python SDK and generate multimodal embeddings from real enterprise datasets, build optimized ANN indexes for millions of vectors with Lance Index, test batch insertion and basic queries on concrete cases like semantic document search, produce a first scalable vector store prototype with performance monitoring, practical exercises on your AI project to validate professional skills.
Advance to implementing hybrid searches combining vectors and metadata in LanceDB, integrate advanced filters and rerankers to boost production accuracy, develop a complete RAG pipeline with local LLMs via Ollama on large datasets, optimize queries for sub-second latency and cloud scalability, deploy with Docker and Kubernetes on real enterprise scenarios, perform comparative benchmarks and a monitoring plan, conclude with a deliverable red thread project certifying your vector database skills.
Target audience
Data engineers, ML engineers, AI developers seeking to upskill on vector stores
Prerequisites
Advanced proficiency in Python, vector embeddings, NoSQL databases, and ML frameworks like LangChain
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