There’s a statistic that should come up in any conversation about enterprise AI: according to Gartner, more than half of machine learning projects never reach production. And of those that do, a significant share start to degrade in performance within the first months, with no one noticing until the damage is done.
The reason, almost always, isn’t that the model is bad. It’s that no one built the scaffolding to keep it running once it left the controlled environment it was trained in. That’s exactly what MLOps comes to solve.
What MLOps is
MLOps (Machine Learning Operations) is the set of practices, processes and tools that makes it possible to take machine learning models to production systematically and keep them running over time. The term combines Machine Learning with DevOps, and borrows from software development the ideas of continuous integration, continuous delivery and lifecycle automation. But adapted to something traditional software doesn’t have: the dependence on data.
An ML model isn’t a program you write once and run the same way forever. It depends on the data it was trained on. And that data changes. A model that predicts a product’s demand well today can be inaccurate in six months if buying patterns have shifted. MLOps is the system that detects that degradation, triggers retraining and redeploys the model (without interrupting operations) before the problem reaches the business.
The three components that make MLOps work
Talking about MLOps as if it were a single thing leads to confusion. In practice, it’s three disciplines that have to coordinate for a model in production to behave with guarantees.
Data engineering
It ensures that the data the model receives in production is equivalent, in quality and distribution, to the data it used during training. When that equivalence breaks (known as data drift), the model’s performance drops.
Data engineering in MLOps defines how to detect that deviation and how to act before it reaches the results the business consumes.
Model engineering
It includes model versioning, automated training pipelines and the tests that validate whether a new model is genuinely better than the one in production before replacing it. In MLOps, a model isn’t deployed because “it works fine locally.” It goes through a process of performance validation, bias detection and stability testing.
If it doesn’t beat the previous model according to defined business metrics, it isn’t deployed. It’s that “simple.”
Platform engineering
It defines the infrastructure that serves the model (how it responds to real-time or batch requests), the systems that monitor it and the governance tools that make it possible to audit what decision the model made, when and with what data.
This last part (governance) will become increasingly relevant as the EU AI Act comes into force and organizations need to demonstrate that their AI systems are auditable.
How it works in practice: maturity levels
Google published a reference framework that divides MLOps maturity into three levels. It isn’t the only one, but it’s the most used in the industry and the most useful for an organization to understand where it stands and what it needs to move forward.
Level 0 (Everything manual)
The data team trains models in notebooks, deploys them ad hoc and there’s no systematic monitoring. It’s the starting point for most companies beginning with AI.
The risk at this level isn’t immediate: the first model usually works. The problem appears when it has to be updated, scaled or support several models at once. That’s where the manual process becomes a bottleneck.
Level 1 (Automated training)
The training pipelines are automated: when new data arrives, the system can retrain the model without anyone intervening. There’s versioning of models and data, and basic monitoring. Deployment may still have manual steps, but the model’s lifecycle is under control.
It’s the level reachable by any data team with the right tools and the will to systematize what they already do.
Level 2 (Full CI/CD for models)
The entire cycle is automated: from drift detection to retraining, validation and redeployment. There’s a system for automatic comparison between the production model and the candidate model (called champion/challenger), and monitoring is proactive.
This is the level reached by organizations that use Artificial Intelligence intensively and need their models to adapt continuously to changes in the business.
MLOps and DevOps: what they share and where they diverge
Anyone coming from the software development world tends to be tempted to put ML models into the same CI/CD pipelines they use for code. The approach is right in spirit, but the particularities of machine learning complicate it in practice.
Traditional software behaves deterministically: if the code doesn’t change, the behavior doesn’t change either. An ML model can degrade in performance without anyone touching a line of code, simply because the input data has evolved. On top of that, reproducing an ML result takes more than the code: you also need the exact data, the hyperparameters and the training environment. And the criterion of “it works” isn’t binary: a model can pass every technical test and still make biased decisions in contexts that weren’t represented in training.
MLOps doesn’t replace DevOps. It extends it. In organizations with a certain technical maturity, both disciplines share platform and culture, but ML teams need additional tools and processes that DevOps alone doesn’t cover.
How we approach model operationalization at BertIA
Taking models to production is, precisely, where most AI projects fail. And it’s the area where, at BertIA, we put the focus from the start of any project.
The starting point isn’t the technology. It’s the company’s operational context: what systems exist, who will maintain the model, what availability is needed and what level of automation is feasible. On that basis, we design the MLOps architecture that fits the existing stack and build the pipelines, the model governance and the monitoring mechanisms that let the model not only reach production, but stay useful when business conditions change.
The result isn’t a well-documented prototype. It’s a system the client’s team can operate, update and audit.
Conclusion
MLOps isn’t an extra layer of complexity on top of AI projects. It’s the necessary condition for those projects to generate value in a sustained way over time. The difference between a company that experiments with AI and one that operates it in production almost always comes down to having the processes, the infrastructure and the rigor of MLOps in place.
If you’re building your first MLOps architecture, or you have models in production that are starting to show signs of deterioration, talk to our experts to review what your specific case needs.
