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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 Training Chroma - Mastering Vector Database Fundamentals 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.
To ensure the quality of the Training Chroma - Mastering Vector Database Fundamentals for AI 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 Training Chroma - Mastering Vector Database Fundamentals for AI 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.
Quick installation of Chroma via pip and creation of a dedicated development environment, exploration of key concepts such as vector embeddings and collections, hands-on practice adding documents with embedding generation via Hugging Face, exercises on data persistence in a local database, configuration of metadata for filtered queries, development of a first simple RAG prototype with immediate trainer feedback to consolidate professional skills.
Deep dive into vector queries using cosine or Euclidean similarity on real enterprise datasets, mastery of dynamic vector updates and deletions, seamless integration of Chroma with LangChain for complete RAG pipelines, practical exercises on performance optimization with HNSW indexing, scalability testing in remote server mode, completion of a deployable hands-on project with documentation and code review for immediate professional application.
Target audience
Data scientists, AI developers, ML engineers seeking to upskill in vector database fundamentals
Prerequisites
Basic knowledge of Python and understanding of vector embeddings
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