podcast

Hackers de Negocios (22): Machine Learning en Predicción de Brotes de Enfermedades

ML predice brotes de enfermedades transformando la vigilancia sanitaria. 4Geeks ofrece experiencia para construir sistemas predictivos robustos y garantizar la privacidad.

Staff

02 May 2025 — 1 min read

Sumérjase en el mundo del Machine Learning (ML) para la predicción de brotes de enfermedades, una herramienta clave que transforma la vigilancia sanitaria de reactiva a proactiva.

Analizamos cómo maneja datos masivos y reconoce patrones complejos para identificar señales tempranas y pronosticar eventos futuros. Conozca los datos diversos que lo alimentan, desde registros clínicos hasta búsquedas en internet y datos ambientales.

Explore los métodos de IA aplicados y los desafíos cruciales a superar, como la calidad de los datos y la privacidad.

Descubra cómo 4Geeks se posiciona como socio de confianza para la transformación digital en este sector crítico, ofreciendo la experiencia necesaria para construir sistemas predictivos robustos, escalar la infraestructura de datos y garantizar la privacidad, ayudando a las agencias de salud pública a anticipar y mitigar el impacto de futuros brotes.

-- Escucha este y otros episodios del podcast Hackers de Negocios en tu plataforma de podcast favorita, incluyendo Apple Podcast, Spotify y YouTube.

A Practical Guide to Implementing Explainable AI (XAI)

For decades, the primary metric for machine learning model success has been predictive accuracy. As CTOs and engineers, we have pushed complex ensembles, deep neural networks, and massive transformer models into production, chasing that extra percentage point of performance. However, this pursuit of accuracy has often come at the cost

How to Implement a Time-Series Forecasting Model with ARIMA

Time-series forecasting—the process of predicting future values based on historical data—is a foundational component of intelligent business operations. For CTOs and engineering leaders, robust forecasting capabilities are critical for optimizing inventory, managing compute resources, projecting financial performance, and detecting anomalies. While modern deep learning models (LSTMs, Transformers) garner

Implementing a Content-Based Image Retrieval System

For decades, digital asset management has relied on a brittle foundation: manual tagging. We search for images using keywords, filenames, and metadata. This approach is fundamentally unscalable, subjective, and fails entirely when metadata is missing or incorrect. As enterprise data balloons with unstructured visual information—from user-generated content to product

How to Build a Real-Time Data Streaming Pipeline with Apache Kafka

In the modern enterprise, the demand for real-time data is no longer a luxury—it's a core business necessity. From powering live analytics dashboards and training machine learning models to enabling event-driven microservices, the ability to capture, process, and react to data as it's generated is

Read more

A Practical Guide to Implementing Explainable AI (XAI)

How to Implement a Time-Series Forecasting Model with ARIMA

Implementing a Content-Based Image Retrieval System

How to Build a Real-Time Data Streaming Pipeline with Apache Kafka