Community
Understanding Regularization in Neural Networks
Regularization prevents a machine-learning model from overfitting during the training process. We'll discuss its concept and key regularization techniques.
Community
Building Production Ready Search Pipelines with Spark and Milvus
A step-by-step process to build an efficient and production-ready vector search pipeline using Databricks Spark and Milvus.
Community
Building an End-to-End GenAI App with Ruby and Milvus
LangChain.rb eliminates the hassle of full-stack developers switching to another programming language when they want to leverage LLMs in their web applications.
Engineering
Building RAG with Self-Deployed Milvus Vector Database and Snowpark Container Services
With Snowflake's Snowpark Container Service (SPCS), users can now run Milvus within the Snowflake ecosystem, allowing them to easily interact with Milvus using data stored in Snowflake.
Engineering
Introduction to LLM Customization
This article discusses several options for customizing LLMs to enhance their performance on specific tasks.
Engineering
How Delivery Hero Implemented the Safety System for AI-Generated Images
This article discussed how Delivery Hero used AI models to generate high-quality food images to improve user experience and conversion rate.
Engineering
A Beginner's Guide to Website Chunking and Embedding for Your RAG Applications
In this post, we'll explain how to extract content from a website and use it as context for LLMs in a RAG application. However, before doing so, we need to understand website fundamentals.
Engineering
How to Pick a Vector Index in Your Milvus Instance: A Visual Guide
In this post, we'll explore several vector indexing strategies that can be used to efficiently perform similarity search, even in scenarios where we have large amounts of data and multiple constraints to consider.
Engineering
Comparing SPLADE Sparse Vectors with BM25
In general, there are two types of vectors: dense vectors and sparse vectors. While they can be utilized for similar tasks, each has advantages and disadvantages. In this post, we will delve into two popular variants of sparse embedding: BM25 and SPLADE.