The article discusses using Large Language Model (LLM) embeddings as features in traditional machine learning models built with scikit-learn. It covers the process of generating embeddings from text data using models like Sentence Transformers, and how these embeddings can be combined with existing features to improve model performance. It details practical steps including loading data, creating embeddings, and integrating them into a scikit-learn pipeline for tasks like classification.
This article explains how to quickly detect data quality issues and identify their causes using Python for ETL pipelines. It discusses strategies to minimize the time required to fix data quality problems.
New Relic's Nic Benders discusses the importance of the Innovation Centre in Hyderabad, their vision for AI, the benefits of their technologies for Indian digital businesses, and more.
