AI Software Engineering

Building AI systems that can be evaluated, understood, and operated in production.

A model is one component of an AI application. The work around it includes context, interfaces, tools, evaluation, infrastructure, and the decisions that determine how it behaves when something fails.

That is one of the two primary lines of this blog. I write about the engineering needed to make those systems observable, testable, and useful under real constraints.

Suggested starting points

MLflow for AI Engineering: Prompts Are Release Artifacts — version prompts, evaluate changes, inspect traces, and make controlled releases.
Anatomy of an MLOps Pipeline, Part 1 — orchestration and reproducible pipelines; continue with deployment and production.
AI Architecture: Training and Inference — hardware and the operational constraints around inference.
Statistical Learning: Foundations — estimation, bias, and variance behind the evaluation work.

For constrained hardware and autonomous workflows, see Edge + Agentic AI. The other primary line is narrative arcs in music: how songs and albums develop ideas, and what computational methods can tell us about that structure.

Published writing

All writing on AI Engineering

Prompts, evaluations, architecture, MLOps, and inference.

08 Sep 2026

MLflow for AI Engineering: Prompts Are Release Artifacts

Prompt versioning, evaluation, GEPA, AI Gateway, and a GCP architecture for operating AI applications with MLflow 3.16.0.

Engineering LLMOps mlflow prompts evaluation tracing genai

05 Apr 2026

AI Architecture - Notions on Training and Inference

A technical breakdown of CPU, GPU, TPU, and Edge AI hardware tradeoffs for training and inference workloads — with real-world cost data and a deep dive into Raspberry Pi 5 + Hailo-10H.

Engineering LLMOps cpu gpu tpu edge-ai inference

12 Mar 2026

Statistical Learning: Foundations, Bias-Variance and the Art of Estimation

A rigorous walkthrough of ISLP Chapter 2 fundamentals — from the formal definition of f(X) to the bias-variance decomposition, Bayes classifiers, and KNN — with Python code, real datasets, and connections to epistemology and learning theory.

Statistical Learning statistical-learning bias-variance knn linear-regression islp

13 Jan 2026

Anatomy of an MLOps Pipeline - Part 1: Pipeline and Orchestration

Part 1: Philosophy, project architecture and orchestration with Hydra + MLflow. Steps for preprocessing, feature engineering, hyperparameter tuning and model registry.

LLMOps MLOps Engineering mlops machine-learning python gcp mlflow

13 Jan 2026

Anatomy of an MLOps Pipeline - Part 2: Deployment and Infrastructure

Part 2: CI/CD with GitHub Actions, W&B vs MLflow comparison, complete containerization with Docker, and production-ready API architecture with FastAPI.

LLMOps MLOps Engineering mlops ci-cd docker fastapi github-actions

13 Jan 2026

Anatomy of an MLOps Pipeline - Part 3: Production and Best Practices

Part 3: Model selection strategies, advanced testing, production patterns, data drift, model monitoring, and production readiness checklist.

LLMOps MLOps Engineering mlops testing production data-drift monitoring

08 Oct 2025

MLflow for Generative AI Systems

September 2026 update: This article is retained as an earlier overview. Its evaluation and judge snippets do not reflect the current GenAI API. For current examples and the prompt lifecycle, read MLflow for AI …

LLMOps MLOps mlflow genai llms tracing evaluation

12 May 2025

The Decline of a Framework

Reflections on TensorFlow in the context of the modern AI engine and the evolving role of Data Scientists

MLOps Edge Computing tensorflow pytorch mlflow edge-ai deep-learning

10 Mar 2025

Raspberry Pi 16GB, Servers, and MLOps

Raspberry Pi 5 (16 Gbs) like a Server

Edge Computing MLOps raspberry-pi mlflow edge-ai llms deployment

23 Feb 2025

MLops into Raspberry Pi 5

A robust implementation of facilities for MLOps development

Edge Computing MLOps raspberry-pi mlflow edge-ai docker kubernetes

21 Nov 2024

Artifact Design and Pipeline in MLOps Part I

Introduction to Artifacts designs

MLOps mlflow artifacts pipeline mlproject argparse

14 Oct 2024

Edge Computing and Edge Machine Learning

A Brief Introduction to AI/Edge Computing

Edge Computing raspberry-pi jetson-nano edge-ai iot

15 Jun 2024

MLOps Guides: A Comprehensive Overview

Machine Learning Operations, deployment strategies, and best practices for production ML systems

MLOps mlops machine-learning deployment mlflow