In this article, you will learn how to automatically extract structured knowledge from raw text and populate a knowledge graph with SPOC quads using a local LLM via Ollama.
Author Archive | Iván Palomares Carrascosa
Monitoring Embedding Drift in Production Scikit-LLM Pipelines
In this article, you will learn what embedding drift is, why it matters for production large language models, and how to implement two practical techniques to detect it.
Multilingual Text Classification with Scikit-LLM and Multilingual Embeddings
Learn how to build a multilingual text classification pipeline using multilingual LLM embeddings and Scikit-learn, without training separate models for each language.
Treating Prompt Templates as Hyperparameters in Scikit-LLM GridSearchCV
In this article, you will learn how to treat prompt templates as tunable hyperparameters for a language model, using scikit-learn’s grid search to find the best-performing prompt for a zero-shot text classification task.
Versioning and Tracking Scikit-LLM Experiments
In this article, you will learn how to build, track, compare, and register scikit-learn pipelines that integrate large language models using Scikit-LLM and MLflow.
Combining LLM Embeddings with Tabular Features in a Unified Scikit-learn Pipeline
In this article, you will learn how to build a unified scikit-learn pipeline that combines text embeddings generated by a lightweight open-source language model with structured tabular features for classification tasks.
Interpretable Text Classification: Probing Scikit-LLM Embedding Spaces
In this article, you will learn how to use probing classifiers, UMAP visualization, and SHAP values to interpret and analyze the quality of text embeddings generated by large language models.
Integrating Agentic AI with Existing Machine Learning Pipelines
In this hands-on article, I will show you how to bridge the gap between reactive machine learning models and proactive AI agents that make decisions and execute actions autonomously.
Managing Small Context Windows in Language Models
In this article, you will learn three practical strategies for managing small context windows in large language models, along with working Python examples that demonstrate how two of those strategies are implemented.
Understanding the Role of Latent Space in Machine Learning Models
This article analyzes, illustrates, and categorizes the core functions and key roles of latent spaces in machine learning models: descriptive, generative, and predictive.