The three layers where ML systems fail

Thu, 12 Feb 2026 00:00:00 +0100

Most ML portfolios show a model that runs once. The interesting engineering is in the part that comes after: keeping it running. In my experience, ML systems fail at three predictable points.

1. Bad data gets in

The model trains or scores on corrupt input and nobody notices until the numbers are wrong three steps downstream. The fix is boring and essential: validate data before it reaches the model — missing values, duplicates, malformed headers, schema drift.

2. The model drifts in production

A model that passed every test on launch day quietly degrades as the world changes underneath it. The first sign shouldn’t be an angry user — it should be a metric. That means monitoring distribution shift (PSI, Wasserstein) and gating deployments on it.

3. The same failure repeats

An outage happens, someone writes a post-mortem, it gets buried in a wiki, and six months later the same thing happens again. Turning past failures into searchable, deployment-gating feedback is the third layer — and the one almost nobody builds.

One principle — reliability — across three layers: data, model, system. That’s the thread running through everything on my projects page.

Why this site exists

Tue, 10 Feb 2026 00:00:00 +0100

This is where I keep my notes on building ML systems that stay working.

I’m a slow, deep learner — I’d rather understand one thing from first principles than skim ten. So instead of a static résumé, I keep a working notebook: what I’m building, what broke, and what I learned fixing it.

If you’re a recruiter or engineer in Berlin looking at my projects, this blog is the “why” behind them. If you’re learning MLOps yourself, maybe some of it saves you a wrong turn.

More soon.

MLOps on Nikhil Adhikari

The three layers where ML systems fail

1. Bad data gets in

2. The model drifts in production

3. The same failure repeats

Why this site exists