Learn to Love Working with Vectors

Machine Learning (ML) represents everything as vectors, from documents, to videos, to user behaviors. This representation makes it possible to accurately search, retrieve, rank, and classify different items by similarity and relevance.

However, working with vectors isn’t always easy. Many engineers and data scientists are just starting to understand how to work with vectors, and many software and infrastructure products were not designed to support them.

These guides will help engineers, data scientists, and even executives understand and harness the power of vectors to build better applications for their business.

Examples

Question Answering

How to build a question-answering application with similarity search and Pinecone.

Semantic Textual Search

How to build semantic search of text documents using vector embeddings.

Video Recommendation

How to create a movie recommender system based on user ratings.

Audio Similarity Search

How to use Pinecone similarity search to build an audio search application.

Benchmarking Vector Similarity Search

How to benchmark the accuracy and performance of Pinecone.

Document Deduplication

How to use Pinecone similarity search to create a simple application to identify duplicate documents.

Extreme Classification

Automatically classify or label data at scale using vector similarity search.

Image Similarity Search

A walkthrough of creating an image similarity search backend service.

IT Threat Detection

How to use Pinecone similarity search to detect threats in IT systems.

Personalized Article Recommender

How to use Pinecone similarity search to create a personalized article or content recommender.

Product Recommendation Engine

Learn how to build a product recommendation engine using collaborative filtering and Pinecone.

ML Engineering

News