Machine Learning on David Bartolomei-Guzmán

Migrating from RunPod to Local Whisper Inference with MLX and a DGX Spark

Sun, 05 Apr 2026 00:00:00 +0000

In my previous article, I described how we use OpenAI’s Whisper model to transcribe radio and TV broadcasts for Monitorea, our media monitoring platform. At the time, we were running inference on RunPod - a serverless GPU platform that lets you deploy ML models without managing hardware. It was the right call to get started quickly. But as we scaled, the economics stopped making sense.

Here’s how we migrated to fully local inference in about a weekend, using MLX on Apple Silicon and a DGX Spark we call Sparky.

Launching Monitorea: AI Agents for Broadcast Media Intelligence

Wed, 01 Oct 2025 00:00:00 +0000

A year ago I published a case study analyzing Share of Voice across Puerto Rico’s AM radio stations using Whisper transcriptions. The article ended with a long list of “future work” — fine-tuning, entity recognition, segment classification, summarization. At the time, those were ideas I wanted to explore. As of this month, most of them are running in production.

Monitorea is now in private beta. Here’s what changed and how we got here.

Case Study: Leveraging Machine Learning for Spoken Media Analysis – Share of Voice of Puerto Rico’s Political Figures in 2024

Tue, 01 Oct 2024 00:00:00 +0000

This is the first in a series of articles where I share my findings exploring Speech-to-Text (STT) ML models to transcribe and analyze spoken content in news media. In this article, I discuss how STT output can be used for automatic mention detection and tracking metrics such as Share of Voice of political figures in Puerto Rico during the 2024 election season.

The Back Story

Before diving into the details, here’s a brief back story on what sparked my interest in this topic. You can skip directly to the results by scrolling down.