Individuals at this level have a basic understanding of Milvus AI's architecture and its role as a vector database. They can perform simple installations and recognize the general use cases for vector databases in AI applications.

Novices can execute basic queries using the Python SDK and manage small datasets within Milvus. They understand embedding vectors' significance in AI and can perform initial data loading and management tasks.

Intermediate users are skilled in implementing data indexing strategies and optimizing query performance for medium-sized datasets. They can integrate Milvus with other AI tools and frameworks, enhancing its utility in various applications.

Advanced practitioners can design and deploy scalable Milvus clusters in cloud environments. They are adept at implementing advanced search algorithms and managing large-scale datasets, ensuring efficient data retrieval and system performance.

Experts develop custom plugins or extensions for Milvus and architect complex AI systems utilizing Milvus for RAG and recommendation systems. They actively contribute to the Milvus open-source community, sharing knowledge and innovations.