When I first started learning machine learning, I believed the model was everything. If my accuracy...
AI product success depends not on model accuracy (94% vs 96%) but systems: data pipelines, monitoring, feedback loops, deployment. For tech managers, allocating budget to MLOps and system design—not just model research—builds dependable, production-grade AI.
When I first started learning machine learning, I believed the model was everything.
If my accuracy wasn't good enough, I searched for a better algorithm.
If training was slow, I blamed my hardware.
If my predictions weren't impressive, I looked for a newer research paper.
Like many aspiring AI engineers, I thought building a better model was the ultimate goal.
How Feature Engineering Taught Me That Better Data Often Beats Better Algorithms When I first...
One of the most expensive debugging mistakes in AI systems is assuming the model is the problem. A...
I recently ran a small evaluation to compare three AI coding assistants. The task sounded...
How I work with AI, and why the system around the model matters more than the model.
Why Accuracy Is Not Enough: Evaluation Metrics Every AI Engineer Should Understand Your...
The AI Wasn't Hallucinating. Our Architecture Was. Everyone talks about choosing the right...
