MLOps is a set of practices that combines DevOps methods with the lifecycle of a machine learning model — from experimentation, through training, to deployment, monitoring and retraining in production. A model that works beautifully in a data scientist’s notebook is only the start — MLOps is what turns it into a service you can actually rely on.
Quick Overview
What you’ll learn from this article:
- How MLOps differs from classic DevOps and why an ML model needs an extra layer of practices
- What an MLOps pipeline looks like, from training data to a model in production
- Why monitoring data drift matters as much as monitoring uptime
- How to split responsibility between a data science team and platform engineering
Who this article is for: data science team leads, ML and platform engineers supporting model deployment, and CTOs planning to scale AI/ML initiatives.
Reading time: 6 minutes
MLOps: taking machine learning models to production
Google Cloud’s documentation points to the key difference between MLOps and traditional DevOps: alongside application code, an ML model’s lifecycle also involves changing data and a changing model itself — and any one of these three elements can independently cause a regression. The code can run flawlessly while the model still stops performing correctly, because the distribution of input data in production has started to diverge from the training data. This is known as data drift, and it’s one of the main reasons ML models need a different maintenance approach than ordinary applications.
The MLOps pipeline — key stages
| Stage | What happens | Who typically owns it |
|---|---|---|
| Data preparation | Validating, cleaning and versioning the training dataset | Data engineering / data science |
| Training and evaluation | Training the model, comparing metrics against the previous version | Data science |
| Deployment (serving) | Packaging the model, exposing it as an API or a batch job | ML engineering / platform |
| Monitoring | Tracking data drift, model accuracy, response latency | ML engineering + data science together |
| Retraining | Automatic or manual retraining once quality degradation is detected | Data science, triggered by monitoring |
The most common mistake teams make early on: treating deployment as the last stage instead of the start of a cycle. A model in production with no monitoring and no retraining plan is a model that quietly stops performing well over time — with no alert, because the application technically doesn’t “crash,” it just returns steadily worse predictions.
How to split ownership between data science and engineering
- Data science owns model quality — feature selection, architecture, evaluating business metrics.
- ML/platform engineering owns deployment reliability — scaling, API availability, model versioning, rollback in case of a problem.
- Shared ownership of monitoring — data science defines what “the model is performing well” means (quality metrics), and engineering provides the infrastructure to measure it in near real time.
- A clear owner of the retraining process — without one designated person, the decision of “should we retrain now” gets diffused across teams and never actually gets made.
Where to start if your team has never run MLOps before
- Start with versioning data and models before building anything more advanced — without it, you can’t reconstruct why a given model behaves the way it does.
- Roll out basic data drift monitoring, even in a simple form (comparing feature distributions weekly).
- Only automate what genuinely repeats — full retraining automation only makes sense after a few manual cycles, once the process is well understood.
Read Also
- Kubernetes NetworkPolicy Fundamentals: Team Roles and Responsibilities
- Cloud FinOps and Cost Control: A Practical Comparison of Approaches
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Frequently Asked Questions (FAQ)
How does MLOps differ from classic DevOps?
In classic DevOps, mainly the application code changes, and its behaviour is largely deterministic. In MLOps, three independent elements change — code, data and the model — and any one of them can independently cause the system to stop performing correctly even though the code stays the same. This requires additional practices, such as data versioning and drift monitoring, that classic DevOps doesn’t cover.
What is data drift and why is it dangerous?
Data drift is when the distribution of input data in production starts to diverge from the data the model was trained on. It’s dangerous because the model doesn’t visibly “crash” — it keeps returning answers, just increasingly less accurate ones, and without dedicated monitoring that can go unnoticed for a long time.
Should model retraining be fully automated from the start?
Not necessarily. Before a team automates the entire retraining process, it’s valuable to go through a few manual cycles first, to properly understand when and why retraining is needed — full automation without that experience can lead to uncontrolled model changes in production.
Who should own the decision to deploy a new model version to production?
A joint decision between data science and engineering works best, based on clearly defined business-metric thresholds set in advance by data science. Without a clearly designated owner of this decision, accountability gets diffused across teams, and deployments either get stuck in endless discussion or reach production without proper validation.