Dataconomy

NOVEMBER 11, 2024

RAG helps models access a specific library or database, making it suitable for tasks that require factual accuracy. What is Retrieval-Augmented Generation (RAG) and when to use it Retrieval-Augmented Generation (RAG) is a method that integrates the capabilities of a language model with a specific library or database.

Data Preparation 203

Data Preparation Database Data Quality Machine Learning 203

AWS Machine Learning Blog

APRIL 30, 2025

In this post, we highlight the advanced data augmentation techniques and performance improvements in Amazon Bedrock Model Distillation with Metas Llama model family. Preparing your data Effective data preparation is crucial for successful distillation of agent function calling capabilities. Notably, the Llama 3.1

AWS 120

AWS AI AI Computer Science 120

Towards AI

FEBRUARY 17, 2025

Last Updated on February 17, 2025 by Editorial Team Author(s): Paul Ferguson, Ph.D. RAFT vs Fine-Tuning Image created by author As the use of large language models (LLMs) grows within businesses, to automate tasks, analyse data, and engage with customers; adapting these models to specific needs (e.g.,

Database 89

Database Data Pipeline Data Preparation Data Quality 89

Pickl AI

APRIL 9, 2025

It provides insights into considerations for choosing the right tool, ensuring businesses can optimize their data integration processes for better analytics and decision-making. Introduction In todays data-driven world, organizations are overwhelmed with vast amounts of information.

ETL 52

ETL Data Warehouse AWS Business Intelligence 52

PyImageSearch

JANUARY 27, 2025

SimHash: LSH for Vector Databases SimHash is a specific type of Locality Sensitive Hashing (LSH) designed to efficiently detect near-duplicate documents and perform similarity searches in large-scale vector databases. Developed by Moses Charikar, SimHash is particularly effective for high-dimensional data (e.g., Huot, and P.

K-nearest Neighbors 101

K-nearest Neighbors Algorithm Data Preparation Database 101

ODSC - Open Data Science

FEBRUARY 5, 2025

Common Pitfalls in LLM Development Neglecting Data Preparation: Poorly prepared data leads to subpar evaluation and iterations, reducing generalizability and stakeholder confidence. Real-world applications often expose gaps that proper data preparation could have preempted. Evaluation: Tools likeNotion.

Data Preparation 52

Data Preparation AI AI Data Scientist 52

PyImageSearch

FEBRUARY 17, 2025

It is designed to enhance the performance of generative models by providing them with highly relevant context retrieved from a large database or knowledge base. ColPali addresses these challenges by streamlining the data ingestion pipeline, enabling efficient document retrieval for visually rich and complex inputs. What Is ColPali?

Deep Learning 97

Deep Learning Deep Learning AI AI 97