Tagged production-ai
20 pieces
- · 7 min read
Model Confidence or Independent Verification
A confidence threshold is the cheapest thing that looks like a safety control, and it fails in the one place you need it: models are most confident on inputs they have never seen. The decision is not what threshold to set, it is whether your task has a cheap grounded check, because that determines whether verification is available at all.
- · 7 min read
What Happens to Your AI Feature at Ten Times the Traffic
An AI feature does not scale like the rest of your product. Its cost is per-request and does not amortise, its latency degrades in a shape most load tests never produce, and its failure mode under saturation is to return plausible garbage rather than to reject the request. All three arrive together, on the day the feature succeeds.
- · 7 min read
What a Minute of Line Downtime Buys You in Architecture
Downtime on an automated line runs from hundreds of thousands to millions of dollars an hour. Convert that to a per-minute figure and it becomes an engineering budget, which changes what is worth building. The interesting consequence is not that you should spend more. It is that the false-positive and false-negative costs are wildly asymmetric, and almost nobody sets the operating point from the plant's economics.
- · 7 min read
Change the Model, Restart the Submission
In most software, retraining is a Tuesday. In a regulated device it can be a regulatory event, and the cost is not the retraining but the clearance cycle it can trigger. That asymmetry should change your architecture, not just your paperwork, because the decisions that determine how often you must retrain are made long before the first submission.
- · 7 min read
What a Two-Week Diagnostic Should Deliver
A diagnostic is not a discovery phase, an architecture review, or a slide deck of observations. It is a finding with evidence attached, a ranked list of what else is wrong, and a scoped fix you could hand to a different vendor. If the deliverable cannot be handed to someone else, it was a sales document.
- · 7 min read
Your Green Dashboard Is Not Evidence
Monitoring answers whether the system is running. Evaluation answers whether it is right. Those are two different dials and almost every organisation has built only the first, which is why the honest sentence after an incident is not that monitoring failed but that nothing was ever watching the thing that broke.
- · 7 min read
How to Tell Your Vendor Is Aiming at the Wrong Thing
The expensive failure is almost never a vendor who cannot execute. It is a competent team executing precisely, on the wrong component. That is much harder to detect, because everything looks like progress, and there are four tells that show up before any code does.
- · 8 min read
What It Costs to Fix a Failing AI System
Nobody in this field answers the cost question straight, so here it is. The price is not set by your system's complexity, it is set by which of four diagnoses you land on, and the four are separated by more than an order of magnitude. Any quote produced before the diagnosis is a quote for the most expensive branch, because that is the only one you can price without looking.
- · 8 min read
The Verifier That Verifies Nothing
Naming a pipeline stage 'verifier' does not make it one. If the stage is another model call asked whether the output looks right, it shares the generator's blind spots, produces an opinion rather than a check, and has almost certainly never rejected anything. There is a ladder of verifier strength with five rungs, and most production systems are on the bottom one while believing they are near the top.
- · 8 min read
Rebuild or Repair? How to Tell Before You Approve Six Months
Two contractors quoted four and six months to rebuild a model. The actual fix took five days and was not the model. The decision between rebuilding and repairing is not a judgement call, it is a diagnosis you can run in an afternoon, and running it first is the difference between a five-day fix and six figures spent reproducing the same failure.
- · 10 min read
The Model Is Five Percent: Where Production AI Actually Fails
Across a decade of systems in defense, medical devices, industrial automation, fintech, and infrastructure at fleet scale, the failure is almost never in the model. It is in the ninety-five percent underneath: the pipelines, the protocols, the drivers, the firmware, the silicon, and above all the seams between them. The clearest proof is that the identical failure shows up in systems with no model in them at all.
- · 7 min read
The Cloud Hides Your AI's Real Failures
Four properties of every model are always present and almost never visible: it extrapolates past its data, it discards most of your input, it is memory-bound on a shared bus, and its confidence off-distribution is undefined. A cloud environment hides all four, because a retry, a redeploy, a bigger box, and a human on a dashboard tomorrow each absorb one. Take the second chance away and they stop hiding at once.
- · 6 min read
Wrong vs Load-Bearing Wrong: When One AI Answer Becomes a Foundation
A system that is wrong costs you one answer. A system that is load-bearing and wrong costs you everything standing on that answer. In a trusted internal tool the output is not the end of anything, it is the start of a chain: a wrong answer becomes a wrong action, the action becomes a commitment, and no stage downstream ever rereads the source. Nothing on your dashboard tells you which of the two you have.
- · 7 min read
Every Metric Is a Proxy: Goodhart's Law for AI Systems
You cannot read forty thousand answers, so you appoint one number as a messenger and send it to check the truth. Every dashboard number is that: a stand-in for something you actually care about but cannot watch directly. The gap between the number and the thing is where trouble hides, and the moment the number becomes the goal, people find ways to move it that have nothing to do with what you wanted.
- · 8 min read
How to Build a Test Set That Actually Predicts Production
A test set drawn at random from your training data measures how much your test set resembles your training set, not whether the system works. Building one that predicts production means holding out whole groups rather than random samples, choosing the grouping variable that matches how your system will actually be deployed, sizing the held-out groups honestly, and checking for the leakage that inflates a score while nothing was learned.
- · 8 min read
Before You Blame the Model: The Four Boring Checks
When an AI system works in testing and fails in production, the model is the last thing to suspect, not the first. Four checks rule out the plumbing before you touch anything interesting: is the same model actually running, is it getting the same input, is the preprocessing identical, and is something quietly timing out or falling back. Most reported model failures die at one of these four.
- · 9 min read
Cleaning Your Input Can Make Your AI Worse
A model does not read your input, it reads a compressed representation of it, keeping only the features it found useful and throwing the rest away. So cleaning up a messy input with a denoiser polishes exactly the information the model already discarded, and moves the features it actually reads in a direction you cannot see, which is why input quality can go up while accuracy goes down.
- · 11 min read
Your AI Is Memory-Bound, Not Compute-Bound (So Tuning the Model Won't Fix Latency)
On fixed hardware, inference latency is set by moving bytes, not by doing math. Most models sit in the memory-bound region with the compute units idle, so the real cost is moving weights and activations across a fixed memory bandwidth. Accuracy, latency, memory, and power are not four goals, they are four views of one thing, the bus, and the worst-case tail is set by contention, not by your model.
- · 12 min read
Confidence Is Not Evidence: Why an AI Model Can't Tell You When to Trust It
A model is geometry fit to data, so past the edge of its training it does not stop, it extrapolates, applying a flat rule that was anchored to nothing, with full confidence. Its certainty is not evidence, because standard training optimizes knowing the answer and never optimizes knowing whether it knows. The decision to trust it has to live outside the model.
- · 15 min read
Why Your AI Works in Testing but Fails in Production
When a model scores 95% in staging and collapses to 44% in production, the model is almost never the cause. The usual fix, rebuilding the model, retrains on the same data and reproduces the same failure. The real problem is what the model learned from the data, and it is usually fixable in days, not months.