[P] Milvus: A big leap to scalable AI search engine
The challenge with data search
The explosion in unstructured data, such as images, videos, sound records, and text, requires an effective solution for computer vision, voice recognition, and natural language processing. How to extract value from unstructured data poses as a big challenge for many enterprises.
AI, especially deep learning, has been proved as an effective solution. Vectorization of data features enables people to perform content-based search on unstructured data. For example, you can perform content-based image retrieval, including facial recognition and object detection, etc.
Now the challenge turns into how to execute effectively search among billions of vectors. That’s what Milvus is designed for.
What is Milvus?
Milvus is an open source distributed vector search engine that provides state-of-the-art similarity search and analysis of feature vectors and unstructured data. Some of its key features are:
Milvus is designed for the largest scale of vector index. CPU/GPU heterogeneous computing architecture allows you to process data at a speed 1000 times faster.
With a “Decide Your Own Algorithm” approach, you can embed machine learning and advanced algorithms into Milvus without the headache of complex data engineering or migrating data between disparate systems. Milvus is built on optimized indexing algorithm based on quantization indexing, tree-based and graph indexing methods.
The data is stored and computed on a distributed architecture. This lets you scale data sizes up and down without redesigning the system.
Milvus is compatible with major AI/ML models and programming languages such as C++, Java and Python.
Billion-Scale similarity search
If you want, you can also try testing 1 billion with Milvus. Here is the hardware requirements.
Milvus has been open sourced lately. We greatly welcome contributors to join us in reinventing data science!
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