What is federated learning? Federated learning is a machine learning (ML) approach that enables multiple devices or systems to train a shared model collaboratively without exchanging raw data. Instead of sending data to a central server, each participant, such as […] The post A Comprehensive Guide to Federated Learning appeared first on The Couchbase Blog.
Why does semantic search need selectivity? Up until now, we’ve viewed a vector embedding as a complete, stand-alone entity – focused entirely on the meaning it encodes. While this enables semantic search, often with a high degree of similarity, it […] The post Semantic Similarity with Focused Selectivity appeared first on The Couchbase Blog.
What is the Capella free tier? Couchbase Capella offers a free tier product with a perpetual, no-cost environment for users to explore and experience the capabilities of Couchbase’s NoSQL database platform. This free tier is not constrained by time limits, […] The post Migrate from Capella Free Tier to a Paid Account in 4 Steps appeared first on The Couchbase Blog.
Data preprocessing is a vital step in machine learning that transforms raw, messy data into a clean and structured format for model training. It involves cleaning, transforming, encoding, and splitting data to improve model accuracy, prevent data leakage, and ensure […] The post The Importance of Data Preprocessing in Machine Learning (ML) appeared first on The Couchbase Blog.
We’re excited to announce that Couchbase is now officially supported as a vector store in Flowise, providing users with a powerful, high-performance solution for managing and retrieving vector data. This new integration combines Flowise’s intuitive, no-code AI workflow capabilities with […] The post Introducing Couchbase as a Vector Store in Flowise appeared first on The Couchbase Blog.
