Technical notes on machine learning engineering and AI systems.
This section includes a weekly series focused on building machine learning concepts from first principles, combining theory, minimal implementations, and system-level considerations.
The goal is not to document tools or APIs, but to clarify how core ideas work, what trade-offs they introduce, and why they matter when systems meet real-world constraints.
After building a baseline model, the next failure many teams encounter is not low accuracy, but false confidence. Training metrics look strong. Loss decreases smoothly. Models appear stable. Yet performance degrades in production, generalization fails, and iteration becomes reactive. In many cases, the issue is not the algorithm itself, but a misunderstanding of how model complexity, data, and evaluation interact. This is where the bias–variance framework becomes essential. Not as a theoretical decomposition, but as a practical lens for interpreting error behavior and deciding what kind of change is actually justified. ...
In the current AI landscape, classical machine learning models are often considered obsolete. Large-scale deep learning and foundation models dominate most discussions, driven by abundant compute, massive datasets, and rapidly evolving tooling. In many teams, reaching for a large model has become the default response, sometimes before the problem itself is fully understood. What is discussed far less is the cost of this default. Infrastructure expenses grow quickly, data requirements escalate, and system complexity increases in ways that are difficult to reason about. In practice, many production failures attributed to model limitations are symptoms of deeper issues, weak signals, noisy or poorly curated data, and objectives that were never clearly defined. ...