The optimal format for storage and retrieval of data is dependent on how it is going to be used. For analytical systems there are decades of investment in data warehouses and various modeling techniques. For machine learning applications relational models require additional processing to be directly useful, which is why there has been a growth in the use of vector databases. These platforms store direct representations of the vector embeddings that machine learning models rely on for computing relevant predictions so that there is no additional processing required to go from input data to inference output. In this episode Frank Liu explains how the open source Milvus vector database is implemented to speed up machine learning development cycles, how to think about proper storage and scaling of these vectors, and how data engineering and machine learning teams can collaborate on the creation and maintenance of these data sets.
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- Your host is Tobias Macey and today I’m interviewing Frank Liu about the open source vector database Milvus and how it simplifies the work of supporting ML teams
- How did you get involved in the area of data management?
- Can you describe what Milvus is and the story behind it?
- What are the goals of the project?
- Who is the target audience for this database?
- What are the use cases for a vector database and similarity search of vector embeddings?
- What are some of the unique capabilities that this category of database engine introduces?
- Can you describe how Milvus is architected?
- What are the primary system requirements that have influenced the design choices?
- How have the goals and implementation evolved since you started working on it?
- What are some of the interesting details that you have had to address in the storage layer to allow for fast and efficient retrieval of vector embeddings?
- What are the limitations that you have had to impose on size or dimensionality of vectors to allow for a consistent user experience in a running system?
- The reference material states that similarity between two vectors implies similarity in the source data. What are some of the characteristics of vector embeddings that might make them immune or susceptible to confusion of similarity across different source data types that share some implicit relationship due to specifics of their vectorized representation? (e.g. an image vs. an audio file, etc.)
- What are the available deployment models/targets and how does that influence potential use cases?
- What is the workflow for someone who is building an application on top of Milvus?
- What are some of the data management considerations that are introduced by vector databases? (e.g. manage versions of vectors, metadata management, etc.)
- What are the most interesting, innovative, or unexpected ways that you have seen Milvus used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Milvus?
- When is Milvus the wrong choice?
- What do you have planned for the future of Milvus?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
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- Linux Foundation/AI & Data
- Pinecone Vector DB
- Vector Embedding
- Reverse Image Search
- Vector Arithmetic
- Vector Distance
- Rotation Matrix
- L2 Distance
- Cosine Distance
- OpenAI CLIP
- CAP Theorem
- Milvus Helm Chart
- Zilliz Cloud
- FPGA == Field Programmable Gate Array
- TPU == Tensor Processing Unit
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