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The Rise of Vector Data
The document discusses the concept of vector data, which is crucial for various applications like semantic search, object recognition, and anomaly detection. It highlights the need for complex algorithms and infrastructure for managing vector indexes to enable efficient similarity searches. Additionally, it promotes Pinecone as a managed service for vector similarity search.
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The Rise of Vector Data
- 1. The Rise of VectorData Edo Liberty Founder & CEO, Pinecone
- 2. What is vectordata?
- 3. What is vectordata? Translation, understanding, Sentiment, Question Answering, Semantic Search, ... Anomaly detection, speech-to-text, music transcription, machinery malfunction, ... Object recognition, deduplication, scene detection, product search, ... Object Vector Task
- 4. Text: BERT, DistilBERT,word2vec, GloVe, ... Audio: wav2vec, mxnet-audio, ... Vision: resnet, alexnet, vgg, squeezenet, densenet, inception, googlenet, mobilenet, ... >> import torchvision.models as models >> model = models.squeezenet1_0(pretrained=True)
- 5. What if wesave the vectors?
- 6. Then we cansearch by similarity
- 7. You’ve seen theresults
- 8. Vectors need anew kind of database Key-Value Graph Vector Document
- 9. A vector indexneeds complex algorithms
- 10. A vector indexneeds complex algorithms http://ann-benchmarks.com/
- 11. A vector indexalso needs complex infrastructure Functionality + Scale ● Sharding ● Replication ● Live Updates ● Namespacing ● Filtering ● Pre/Post processing Production readiness ● High Availability ● Persistence ● Consistency ● Monitoring ● Alerting ● Support
- 12. You can leveragevectors through a managed service Vectors Similarity search as a service (Pinecone.io) Application or notebook
- 13.
- 14. Thank you! Pinecone.io —Similarity search as a service