SHORT ANSWER
MLOps (machine learning operations) is the set of practices and tools for building, deploying, monitoring and updating machine learning models in production. It applies DevOps ideas such as version control, automated tests and CI/CD to data, models and experiments. A sensible start is four pieces: experiment tracking, a model registry, one automated deployment pipeline and drift monitoring.
MLOps, short for machine learning operations, combines machine learning, data engineering and DevOps to take models from experiment to production and keep them working. It covers the whole lifecycle: preparing data, training and tracking experiments, validating and registering models, deploying them, monitoring how they behave and retraining when performance drops.
How does the MLOps lifecycle work?
Data and features: version your training data and compute features in reusable, tested pipelines.
Experiment tracking: log parameters, code versions, data versions and metrics for every run, for example with MLflow.
Model registry: store approved models with version, lineage and stage, such as staging or production.
CI/CD: automated tests check code, data and model quality before the model ships as a batch job or API.
Monitoring: track data drift, prediction quality, latency and cost in production, with alerts when thresholds are crossed.
Retraining: retrain models on a schedule or when monitoring flags a drop.
Why does MLOps matter for enterprises?
Many machine learning projects never reach production. Many that do lose accuracy as customer behaviour, prices or processes change. MLOps makes models reproducible, auditable and maintainable by a team instead of one data scientist. It also helps with regulation. The EU AI Act, in force since August 2024, sets documentation, logging and monitoring duties for high-risk AI systems. This is general information, not legal advice.
Manual ML vs. MLOps
| Manual approach | With MLOps |
|---|---|---|
Reproducing a model | Often impossible | Tracked code, data and parameters |
Deployment | Manual hand-over, often slow | Automated, repeatable pipeline |
Detecting drift | When users complain | Automated monitoring and alerts |
Audit trail | Scattered | Registry and lineage |
Where should you start?
You don’t need a big platform on day one. Start with experiment tracking, a model registry, one automated deployment pipeline and basic monitoring for your most important model. Add feature stores, automated retraining and finer monitoring as the number of models grows. Use the tooling already built into your data platform to keep cost and complexity down.
What we see in data projects is that the model is rarely the first thing to break. The input data is. A source system renames a field, a supplier changes a unit or a pipeline quietly drops rows, and predictions drift while every service still reports green. That’s why data checks belong in the same pipeline as model tests, and monitoring belongs in the first release, not a later phase.
How RUBICON helps with MLOps
We build data and machine learning pipelines on Databricks and Azure with CI/CD and monitoring from the start. See our AI and machine learning services, or our architects can look at your current model setup with you.
Related terms
Frequently asked questions
What is the difference between MLOps and DevOps?
DevOps automates building, testing and releasing software, where code defines behaviour. MLOps adds what makes machine learning harder: behaviour also depends on data and training. So teams version datasets and models, track experiments, validate model quality and monitor for data drift. MLOps builds on DevOps practices rather than replacing them.
What tools are used for MLOps?
Common tools include MLflow for experiment tracking and a model registry, Databricks and Azure Machine Learning as managed platforms, feature stores, orchestration with Airflow or Databricks Workflows, CI/CD with GitHub Actions or Azure DevOps, and tools that monitor data drift and model performance. Many companies start with what their data platform already includes.
What is LLMOps?
LLMOps applies MLOps practices to applications built on large language models. The focus shifts from training models to managing prompts, retrieval pipelines, model versions, evaluation datasets, guardrails, cost and latency. Monitoring covers answer quality, hallucinations and security issues such as prompt injection. Many teams treat it as an extension of their MLOps practice.
When does a company need MLOps?
As soon as a model affects real decisions or customers. You can maintain a single model by hand. Once you have several models, regular retraining or regulatory duties, manual processes start to fail. Setting up basic tracking, a model registry, automated deployment and monitoring early costs far less than fixing failures later.
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