What changed
This blog used to read like a broad notebook on machine learning systems. It is becoming something more focused.
The center is now AI Software Engineering: the discipline of building AI systems that are understandable, reproducible, observable, and production-ready once models meet infrastructure, interfaces, and real users.
Inside that center, four themes matter most here:
- LLMOps as the evolution of MLOps
- GCP as an operating context for production AI
- Edge computing and agentic AI
- Music analysis with NLP and LLMs
That is the map for everything else on the site.
Reading paths
1. LLMOps: what changes when the model becomes part of a larger system
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
2. AI Software Engineering: the system around the model
Training a model is only one slice of the problem. The more durable work is in architecture, evaluation, testing, interfaces, cost, monitoring, and operational tradeoffs.
- 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.
3. GCP, edge computing, and agentic systems
This is where the blog moves from abstract engineering language to concrete system constraints: hardware, latency, cost, deployment surface, and the problem of autonomy outside a single request-response loop.
- GCP posts — architecture and infrastructure notes tagged around Google Cloud
- Edge + Agentic AI — the dedicated hub for this thread
- Edge Computing and Edge Machine Learning — a conceptual entry point into inference beyond centralized cloud assumptions
- Raspberry Pi 16GB, Servers, and MLOps — what edge infrastructure looks like when you treat small hardware seriously
- MLops into Raspberry Pi 5 — hands-on infrastructure thinking for constrained environments
4. Music, NLP, and LLMs
This is the parallel line of work that keeps the technical writing honest. Music is a strong test case because language models often look smarter than they are when metaphor, repetition, symbolism, and structure are doing the real work.
- 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 read the site now
If you want the clearest picture of the blog’s editorial direction, go next to AI Engineering.
If you want the research line that is least like everyone else’s AI writing, go to Music + NLP.
If you want updates when something genuinely new is ready, the best formats are RSS and the newsletter. I write when I have something worth saying, not to satisfy a content calendar.