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Software either works or it breaks visibly. Machine learning has a third state: still running, still returning predictions, quietly getting worse.
Input distributions shift. A feature pipeline changes upstream and the model receives values it never saw in training. Seasonality moves. The predictions look plausible, the service returns 200, and the business impact accumulates for weeks before anyone connects a falling metric to a model nobody has looked at since launch.
MLOps exists to make that failure visible. Monitoring for drift rather than just uptime, reproducible training so you can explain what produced a given model, and a retraining path that doesn’t require the original data scientist to still be at the company.
The gap between a model that works in a notebook and one that still works on a Tuesday morning six months later is mostly engineering, and it is rarely the part anyone budgeted for.
Monitoring input distributions and prediction quality, not just latency and error rate.
Versioning data, code, and model artefacts together. Being able to rebuild a model from six months ago.
Automated, scheduled, and validated. Retraining shouldn't be a manual afternoon.
Batch versus real-time, latency budgets, scaling. Different problems with different answers.
When they're worth the overhead and when they're premature. Frequently oversold.
GPU utilisation and inference cost. Easy to waste substantially.
The signature MLOps failure is a model that degrades for months without anyone noticing, and the reason it goes unnoticed is that every dashboard stays green.
Take a fraud model trained on last year’s transactions. Payment mix shifts toward a new provider. The feature distribution moves, the model has never seen this pattern, and its precision falls from 0.82 to 0.60 over a quarter. Latency is unchanged. Error rate is zero. Uptime is perfect. The only signal is a slow rise in chargebacks that finance attributes to market conditions. By the time anyone links the two, you’ve lost two quarters of decisions and have no way to reconstruct what the model was doing in month one — because the training data wasn’t versioned and the notebook that produced it has been edited since.
Training-serving skew produces the same silence. A feature computed one way in the training pipeline and slightly differently in the serving path — a different default for missing values is enough — gives a model that scored well offline and underperforms in production, permanently, with nothing in the logs to explain it.
None of this is exotic. It’s the normal outcome of deploying a model and treating that as completion.
The failure is quiet, which is what makes it expensive. A drifted model still returns confident predictions in the expected format, so nothing alerts and nothing breaks — the decisions made on those predictions simply get worse for months before anyone connects the two.
Mid-level (3–5 years). Operates an existing platform. Comfortable with containers, pipelines, and deployment. Needs direction on architecture.
Senior (5–8 years). Designs the platform. Makes serving, monitoring, and orchestration decisions. Sets the reproducibility standard.
Lead (8+ years). Owns ML platform direction across teams, handles governance and audit requirements, makes build-versus-managed-service calls.
Getting models into production. Data science teams with notebooks and no deployment path. The most common request.
Monitoring and alerting. Models already live with no visibility into whether they still work.
Training automation. Moving from manual retraining to scheduled, validated pipelines.
Platform build. Standing up the infrastructure a growing data science team needs. Often alongside DevOps engineers.
Cost optimisation. GPU and inference spend that has grown without scrutiny.
Inference cost reduction. Batching, caching, and right-sizing instances. Often the fastest measurable win available in a first engagement.
Week one. They inventory what is actually running: which models, which versions, what produced them, and who would know if one stopped working. On most teams that inventory is incomplete, and finding out is the point. Expect questions about whether training data is versioned, whether a model from six months ago could be rebuilt, and what currently triggers a retrain.
Weeks two to four. They instrument one model properly end to end — input distribution monitoring, prediction distribution, and a link to a business metric — and put a retraining path behind it that runs on a schedule with validation gates. One model done properly is a template for the rest and is far more useful than partial coverage everywhere.
The warning sign is infrastructure with no monitoring attached. An engineer who spends the first month standing up Kubeflow or a feature store, before anything is being watched, has optimised for platform completeness over the problem you actually have.
Expect monitoring before automation. There is little value in retraining on a schedule when nobody can say whether the current model is still performing. The first month should establish what is being measured — input distributions, prediction distributions, and whatever ground truth arrives late — because everything after that is guesswork without it.
Monitoring uptime and calling it monitoring. Latency, error rate, and availability tell you the service is running, not that it’s correct. You need input drift, prediction drift, and ideally a delayed comparison against ground truth. Ask a candidate how they’d detect a model getting worse while returning 200s — it’s the most revealing question in this category.
Buying a feature store before you need one. They earn their considerable overhead when multiple teams share features across training and serving. With two models and one team, a well-tested shared transformation library does the same job. Vendors are persuasive here; the honest answer is usually not yet.
Expecting a DevOps engineer to cover it. The container and pipeline skills overlap substantially, which is why the substitution seems reasonable. What doesn’t transfer is model versioning, drift detection, and the statistical judgement to know when a distribution shift matters. A strong DevOps engineer can grow into MLOps in months, not weeks — plan for that rather than discovering it.
Building the platform before there is anything to run on it. Feature stores, registries, and orchestration all make sense at a certain scale and are pure overhead below it. A team with two models and a serving latency problem needs the latency fixed, not a platform. Let the third or fourth model justify the infrastructure rather than the first.
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