MLOps applies software delivery and operations practices to machine-learning systems, whose behavior depends on code, data, and trained models. It connects data preparation, model development and evaluation, deployment, and ongoing monitoring so teams can operate ML reliably and improve it as conditions change.
What is MLOps?
MLOps is a set of practices and an engineering culture for automating and simplifying machine-learning workflows and deployments. AWS describes it as unifying ML application development with system deployment and operations; Google Cloud similarly emphasizes automation and monitoring across integration, testing, release, deployment, and infrastructure management. See AWS’s MLOps overview and Google Cloud’s MLOps architecture guide, last reviewed August 28, 2024.
The central difference from ordinary software delivery is that an ML system’s output depends not only on application code but also on the data used to train and serve it, and on the model learned from that data. Operating it therefore means managing and checking all of those parts—not simply deploying a program and keeping its servers online.
How MLOps differs from DevOps
MLOps uses familiar DevOps principles such as collaboration, automation, testing, and dependable releases, then extends them to the additional assets and failure modes of machine learning.
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| Practice area | DevOps focus | Additional MLOps concern |
|---|---|---|
| Changes | Application code and infrastructure | Code plus data, model versions, and training pipelines |
| Validation | Tests that software changes behave as intended | Data validation and evaluation that establish whether a trained model is suitable for deployment |
| Production health | Service availability and technical performance | Those service signals plus predictive performance and changes in data or input-to-outcome relationships |
| Improvement | Release fixes and updates | Investigate model or data issues and, when warranted, evaluate and deploy a newer model |
A service can remain technically healthy while its predictions become less useful: input data may change, or the relationship between inputs and outcomes may shift. Model-aware monitoring complements conventional operational checks rather than replacing them.
What the MLOps lifecycle includes
A practical lifecycle makes the work repeatable from input data through production feedback. The exact level of automation depends on the system and team; continuous retraining is an option, not a requirement for every project from day one.
- Prepare and validate data. Collect and transform data for the task, then check that pipeline inputs meet expected conditions. Repeatable preparation makes it easier to understand what data a model used.
- Train candidate models. Run training workflows with trackable inputs and outputs so candidates can be compared and reproduced.
- Evaluate and validate candidates. Assess candidates on evaluation data and compare results with an appropriate baseline. Promote a model only when it meets the project’s criteria for deployment.
- Automate appropriate checks and releases. Continuous integration (CI) checks code and pipeline changes. Continuous delivery or deployment (CD) moves validated changes toward production. Continuous training can rerun training when data changes or other conditions warrant it; teams can introduce it as their needs and controls mature.
- Serve predictions. Package and release the chosen model through a serving pattern suited to the application’s latency and environment needs.
- Monitor and feed back findings. Track predictive performance and relevant production signals. Investigations, new data, or a deterioration in performance may lead to another evaluation or training cycle.
Google Cloud’s MLOps architecture documentation describes automation and monitoring throughout system construction and operation. Its Practitioners Guide to Machine Learning Operations also covers continuous training, serving, dataset and feature management, and model governance.
How ML models are deployed
There is no single serving pattern for every model. Google Cloud’s architecture guide describes online prediction services, embedded models on edge or mobile devices, and batch prediction. The right choice depends on when predictions are needed, where they must run, and how the system fits into existing infrastructure.
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|---|---|---|
| Online service | An application requests predictions from a deployed service, often through an API or microservice. | Consider response-time needs, service availability, integration, and who operates the serving infrastructure. |
| Edge or mobile | The model is embedded on a device and makes predictions there. | Consider the target device and its constraints, as well as how model updates and monitoring will work in that environment. |
| Batch prediction | The system processes a collection of inputs together rather than returning a prediction for each live request. | Consider when results are needed, how often batches run, and how outputs reach downstream systems. |
Compare options by serving mode and latency needs, target environment, integration with existing infrastructure, operational control, lifecycle coverage, and the amount of platform management the team wants to own. Packaging can help make model behavior and dependencies easier to manage: for example, MLflow’s model-serving documentation describes packages with metadata such as dependencies and an inference schema, as well as deployment targets including local environments, cloud services, and Kubernetes clusters. Those are documented capabilities of one project, not a performance comparison or a recommendation that every team use it.
What should model monitoring cover?
Monitoring should answer two different questions: is the service operating, and are its predictions still appropriate for the task? The first is a conventional operations concern; the second requires attention to ML-specific behavior and context.
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- Service and infrastructure signals: check that the serving system and its supporting infrastructure are functioning as expected.
- Predictive performance: assess whether model results continue to meet the task’s criteria as evidence becomes available.
- Input data: validate production data and investigate changes that may affect predictions.
- Model and pipeline changes: keep track of which model and relevant pipeline version produced predictions so that problems can be investigated.
- Actionable alerts: define what conditions should prompt investigation, a rollback, or a new evaluation and training cycle.
For generative-AI operations, Google Cloud also identifies drift, skew, and performance decay as conditions that can trigger alerts. Monitoring is useful when it leads to an owner and a defined response; an alert without a way to investigate or act on it does not close the lifecycle loop.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How MLOps relates to LLMOps
MLOps practices can be adapted to applications built on foundation models, but operating an LLM-powered application includes concerns beyond the underlying model lifecycle. The shared ground includes validating inputs, evaluating behavior, deploying services, and monitoring production. Application-level evaluation, prompt management, and tracing need their own attention.
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MLflow describes LLMOps as building, deploying, monitoring, and maintaining LLM applications, with concerns including tracing, evaluation, prompt management, and production monitoring. Its overview is available at What is LLMOps?. These concerns complement MLOps; they do not make traditional data, model, and service operations unnecessary.
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