What this is and why it exists
The gap between a model that passes a notebook cell and one that runs at 2am without breaking is wider than most people are told. Closing that gap requires a specific kind of thinking — systems design, operational discipline, and a willingness to engage with the unglamorous parts of production ML. That’s what this blog is about.
I’m Carlos. My background includes the United Nations, IDB, Yale, Mercado Libre, and Globant — plus a few years teaching ML across Latin America and Spain. The through-line is the same problem in different institutional forms: making machine learning systems that actually work, for the people who need to use them.
I write here when I have something specific enough to be useful. Topics cluster around three technical pillars — LLMOps, AI software engineering, and GCP — and one parallel research thread that I find equally interesting: applying NLP and LLMs to music analysis.
Reading paths
LLMOps — MLOps for the LLM era
The MLOps discipline that emerged around 2020 was designed for classical ML: feature pipelines, model registries, batch inference, drift detection on tabular data. LLMs broke most of those assumptions. This is where I work through what the new discipline looks like.
- Anatomy of an MLOps Pipeline — Part 1: Orchestration — start here: Hydra + MLflow, reproducible pipelines, 7-step GitHub-triggered pipeline from scratch
- Anatomy of an MLOps Pipeline — Part 2: Deployment — CI/CD, containerization, rollback in 30 seconds
- Anatomy of an MLOps Pipeline — Part 3: Production — monitoring, drift detection, production readiness
- MLflow for Generative AI Systems — tracing, evaluation, and versioning when “the model” is code + prompts + tools
AI Software Engineering
Training a model is 20% of the work. The other 80% is the system around it. These posts cover that layer.
- AI Architecture: Training and Inference — when to use CPU, GPU, TPU, or edge hardware. Real cost data. Why inference costs 15–20× more than training over a model’s lifetime.
- Statistical Learning: Foundations, Bias-Variance and the Art of Estimation — the math that underlies every model evaluation. Derived from first principles, not borrowed from a textbook summary.
Music & AI
What happens when you apply NLP, embeddings, and LLMs to music? I’ve been exploring this seriously — with statistical rigor and genuine curiosity about where the tools fail.
- Attention Windows: Narrative Cognitive Load in Beatles vs Pink Floyd — the founding piece of this thread. A novel framework for measuring semantic persistence in lyrics, an unexpected result, and a theoretical account of why transformer embeddings systematically fail at abstract thematic analysis.
How to follow
The best formats are RSS and the newsletter. I write when I have something worth saying — not on a fixed schedule.