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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 Qdrant - Scaling Vector Databases in Production 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 Qdrant - Scaling Vector Databases in Production 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 Qdrant - Scaling Vector Databases in Production training is carried out through:
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.
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.
Configure advanced Qdrant collections with dynamic sharding and multi-node replication to handle millions of vectors in high availability, implement hybrid search combining vector similarity and keyword filtering on real enterprise datasets, integrate RAG pipelines with LangChain and Hugging Face embeddings for precise semantic responses, optimize performance via payload indexing and quantization, deploy a Docker/Kubernetes cluster with TLS and authentication, monitor via Prometheus/Grafana on concrete AI cases, produce a deployable deliverable with a personalized optimization plan.
Target audience
Data engineers, ML engineers, and AI developers in companies seeking to build expertise on vector databases
Prerequisites
Proficiency in Python, vector embeddings, and distributed NoSQL databases
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