DevOps and MLOps engineers may work with the same cloud platforms, containers, automation tools, and deployment pipelines. Put them in the same meeting, though, and they’ll usually be responsible for very different parts of the production environment.
A DevOps engineer focuses on helping software teams build, release, and operate applications reliably. An MLOps engineer applies many of those practices to machine learning systems while also managing training data, model versions, experiments, retraining workflows, and model performance after deployment. That additional layer is what makes the MLOps vs. DevOps comparison more complex than a simple difference in job titles.
The distinction matters when you’re deciding who to hire. A company struggling with slow software releases, unstable infrastructure, or limited observability may need a DevOps engineer. A team that already has models sitting in notebooks, or models in production that are difficult to monitor, reproduce, and update, may need someone who understands what an MLOps engineer does.
The right hire depends on what your team is putting into production and where the workflow starts breaking down. In this guide, we’ll examine the difference between MLOps and DevOps across pipelines, testing, monitoring, infrastructure, ownership, and collaboration. You’ll also see when one engineer may cover both areas, when specialization becomes valuable, and how to choose between an MLOps engineer vs. a DevOps engineer based on your company’s actual needs.
MLOps vs. DevOps at a Glance
The easiest way to understand the difference between MLOps and DevOps is to look at what each discipline focuses on.
DevOps supports the software development lifecycle, helping teams test code, deploy applications, manage cloud infrastructure, and maintain reliable systems. MLOps supports the machine learning lifecycle, which includes many of the same foundations plus data validation, experiment tracking, model training, deployment, monitoring, and retraining.
Both disciplines improve the path from development to production, but they manage different types of change. A DevOps pipeline usually responds to changes in code or infrastructure. An MLOps pipeline may also respond to new training data, declining model accuracy, data drift, or updated business requirements.
The two functions often meet in the same production environment. A DevOps engineer may provide the cloud infrastructure, containers, access controls, and deployment standards that support an AI product. An MLOps engineer then builds the workflows needed to train, register, release, observe, and update its models.
That’s why the MLOps vs. DevOps decision isn’t always an either-or choice. The real question is which lifecycle needs dedicated ownership and whether your existing engineering team has the specialized experience to manage it.
What Is DevOps?
DevOps is a set of practices that brings software development and IT operations closer together. Its goal is to help teams release software more frequently, maintain stable infrastructure, and respond to production issues faster.
A DevOps engineer builds and maintains the systems that move code from development into production. Depending on the company, that may include setting up CI/CD pipelines, automating cloud infrastructure, managing containers, configuring monitoring tools, and improving how engineering teams handle deployments and incidents.
In a typical DevOps workflow, developers submit a code change, automated tests check it, and a continuous integration and continuous delivery pipeline prepares it for release. Once the application is in production, the DevOps team monitors metrics such as uptime, latency, error rates, resource usage, and deployment health.
The assets under DevOps ownership commonly include:
- Application code and configuration files
- Cloud infrastructure and networking
- Containers and orchestration environments
- CI/CD pipelines
- Access controls and secrets
- Application logs, alerts, and performance dashboards
DevOps engineers may work with tools such as GitHub Actions, Jenkins, Terraform, Docker, Kubernetes, AWS, Microsoft Azure, and Google Cloud. However, the role is defined by the systems and outcomes it owns rather than a specific toolset.
DevOps becomes especially valuable when software releases depend on manual steps, environments behave inconsistently, developers spend too much time managing infrastructure, or production problems take too long to detect and resolve. Its primary focus remains the software development lifecycle, even when the application contains an AI-powered feature.
What Is MLOps?
MLOps, short for machine learning operations, is the practice of building reliable processes for training, deploying, monitoring, and updating machine learning models in production.
It applies many DevOps principles, including automation, version control, continuous integration, infrastructure management, and observability to the machine learning lifecycle. The difference is that an MLOps workflow must account for more than application code. It also manages the data, features, experiments, model versions, and evaluation results that influence how an AI system behaves.
An MLOps engineer helps turn a model created in a notebook or development environment into a repeatable production system. Their work may include building automated training pipelines, creating model registries, validating data, deploying inference services, and tracking how models perform after release.
A typical MLOps pipeline may include:
- Collecting and validating training data
- Creating and versioning features
- Training and evaluating candidate models
- Registering approved model versions
- Deploying models to production
- Monitoring predictions, drift, latency, and cost
- Retraining or replacing models when performance changes
The central challenge is that a model’s behavior can change even when the surrounding application code stays the same. Customer behavior may shift, incoming data may follow a different pattern, or the relationship between inputs and outcomes may evolve. These changes can gradually reduce model quality without causing a conventional software error.
That’s why MLOps monitoring goes beyond uptime and server health. Teams may also track data drift, prediction distributions, feature quality, model accuracy, and retraining triggers. This broader scope makes the MLOps pipeline vs. DevOps pipeline distinction especially important for companies running custom AI and machine learning systems in production.
8 Key Differences Between MLOps and DevOps
MLOps and DevOps share foundations such as automation, version control, cloud infrastructure, CI/CD, and production monitoring. Their responsibilities begin to diverge when you examine what moves through each pipeline, what can cause a production failure, and what teams must monitor after deployment.
The following differences explain why a conventional DevOps workflow may support an AI product but rarely covers the complete machine learning lifecycle on its own.
1. Software Lifecycle vs. Machine Learning Lifecycle
DevOps primarily manages the software development lifecycle. A typical workflow begins when a developer changes application code, configuration, or infrastructure. Automated tests validate the change, a CI/CD pipeline prepares the release, and the updated application moves into production.
MLOps manages a broader and more iterative machine learning lifecycle. The process may begin with new data, an experiment, a retraining schedule, or a decline in model performance rather than a direct code change.
A typical MLOps lifecycle includes:
- Collecting and validating data
- Preparing features for training
- Training candidate models
- Evaluating and comparing results
- Registering an approved model
- Deploying it to production
- Monitoring predictions and model behavior
- Retraining, replacing, or retiring the model
The cycle may repeat whenever customer behavior changes, new information becomes available, or the model falls below a defined performance threshold. This is why Google Cloud’s MLOps framework includes continuous training alongside continuous integration and delivery.
Machine learning systems also produce more types of production artifacts. A DevOps pipeline commonly manages source code, infrastructure definitions, containers, and configuration files. An MLOps pipeline may also need to track datasets, feature definitions, experiment results, model files, evaluation reports, and model metadata.
Consider a fraud-detection platform. A software update might change how the application displays a risk score, while a model update could change how that score is calculated. The first change follows the software lifecycle. The second requires teams to identify which data trained the model, how it performed during evaluation, which version was approved, and whether its predictions remain reliable after deployment.
That broader chain of dependencies is one of the clearest differences between MLOps and DevOps. DevOps keeps software releases reliable, while MLOps keeps the entire path from data to production predictions traceable and repeatable.
2. Code Versioning vs. Code, Data, and Model Versioning
Version control is central to both DevOps and MLOps, but the number of assets that need to stay connected is much greater in a machine learning system.
In DevOps, teams typically use Git to track application code, configuration files, infrastructure definitions, and deployment changes. When a release causes a problem, engineers can identify the commit, restore a previous version, and redeploy the application.
MLOps requires that same traceability across a wider chain of dependencies. A production prediction may be influenced by:
- The source code used to train the model
- The training and validation datasets
- Feature engineering logic
- Model architecture and hyperparameters
- Experiment results
- The approved model version
- Runtime configuration
- The environment where inference takes place
A model file on its own doesn’t explain how the model was created or why it produced a specific result. Teams need to connect each model version to the data, code, parameters, and evaluation metrics behind it.
For example, imagine that a customer churn model begins classifying too many active customers as likely to leave. The MLOps team must be able to determine whether the change came from new training data, an altered feature definition, a different model configuration, or a deployment issue.
This is where tools such as model registries, experiment trackers, data versioning systems, and metadata stores become part of the MLOps workflow. They give teams a record of what was trained, what was approved, what entered production, and how each version performed.
A clear lineage also makes rollback more reliable. Rather than simply restoring the previous application build, the team may need to restore a specific model version together with its feature logic and serving configuration.
DevOps version control answers, “Which software change did we release?” MLOps lineage must also answer, “Which combination of code, data, features, and model produced this behavior?” That distinction becomes increasingly important as companies deploy more models across customer-facing products and internal workflows.
3. CI/CD vs. CI/CD/CT
DevOps and MLOps both use automation to move changes from development into production. The difference is that machine learning pipelines often need one more continuous process: training.
In a DevOps environment, CI/CD usually includes:
- Continuous integration (CI): Developers merge code changes frequently while automated tests check quality, security, and compatibility.
- Continuous delivery or deployment (CD): Approved software changes move through staging and into production with minimal manual work.
This workflow is usually triggered by a change to application code, infrastructure, or configuration. Once the pipeline validates the change, the team can release the new version.
MLOps CI/CD includes those same foundations while adding model-specific steps such as data validation, model evaluation, model registration, and deployment approval. It may also include continuous training (CT), a process that automatically retrains a model when new data becomes available or another predefined condition is met.
Common continuous training triggers include:
- A scheduled retraining cycle
- New labeled data
- Changes in incoming data
- Declining model accuracy
- Data or concept drift
- Updated features
- New regulatory or business requirements
For example, a recommendation model may perform well when it first enters production. As customer preferences, inventory, and purchasing patterns change, its recommendations can become less relevant. A continuous training pipeline can collect approved data, retrain the model, evaluate the new version against the current one, and promote it only when it meets the company’s performance standards.
That final evaluation step matters. Automating retraining doesn’t mean every newly trained model should enter production automatically. MLOps teams often use quality thresholds, offline testing, approval gates, and controlled rollouts before replacing an existing model.
The Google Cloud MLOps architecture describes this progression from manual model development to automated ML pipelines with continuous training and delivery. The exact setup depends on the company, but the principle stays the same: production ML systems need a repeatable way to update models as their data and operating conditions evolve.
This makes the MLOps pipeline vs. DevOps pipeline distinction much clearer. DevOps CI/CD delivers software changes reliably, while MLOps CI/CD/CT also manages how models are retrained, evaluated, approved, and replaced over time.
4. Software Testing vs. Data and Model Validation
DevOps testing focuses on whether an application behaves as expected. Before a release reaches production, teams may run unit tests, integration tests, security checks, performance tests, and end-to-end tests to confirm that the software works reliably.
MLOps includes those checks while adding tests for the data and model behind the application.
A machine learning system can run without generating an error and still produce poor results. The API may respond on time, the infrastructure may remain healthy, and every service may technically be available, even while the model’s predictions become less accurate.
That’s why MLOps validation may cover:
- Data schemas and missing values
- Unexpected changes in feature distributions
- Training and serving consistency
- Model accuracy and other evaluation metrics
- Performance across relevant user groups
- Reproducibility between training runs
- Inference latency and resource usage
- Results against the current production model
- Business-specific acceptance thresholds
Consider a loan-risk model that successfully processes every request but begins approving applicants differently because an upstream data source changed its formatting. From a conventional software perspective, the system may appear healthy. From an MLOps perspective, the incoming data has changed in a way that could affect model quality and business outcomes.
MLOps testing asks whether the system works technically and whether the model still behaves acceptably. That second question requires teams to define what an acceptable model looks like before deployment.
A new model may need to outperform the current version, remain within latency and cost limits, and meet fairness or compliance requirements before it can move forward. Depending on the use case, teams may also require manual approval from data science, engineering, risk, or product leaders.
This creates a different release process. A passing software test confirms that code performs according to its specifications. A passing model evaluation confirms that a model meets statistical, operational, and business standards using the available data.
DevOps protects software quality during release, while MLOps expands that protection to the data, predictions, and decision-making behavior of the model.
5. Application Monitoring vs. Model Monitoring
DevOps monitoring focuses on the health and reliability of software in production. Teams watch metrics such as uptime, latency, error rates, failed deployments, CPU usage, memory consumption, and network performance.
MLOps teams monitor those same operational signals while also tracking whether the model continues to produce useful and reliable predictions.
This distinction matters because a model can degrade gradually without causing a visible application failure. The service may remain available, requests may complete successfully, and infrastructure dashboards may look normal while the quality of the model’s output declines.
MLOps monitoring may include:
- Data drift
- Concept drift
- Prediction distributions
- Feature quality and availability
- Model accuracy
- Confidence scores
- Inference latency
- Resource consumption
- Cost per prediction
- Differences between predicted and actual outcomes
Data drift occurs when the information entering the model begins to look different from the data used during training. Concept drift happens when the relationship between inputs and outcomes changes over time.
For example, a demand forecasting model trained on historical purchasing behavior may become less accurate after a major change in customer preferences. The application can continue generating forecasts normally, but those forecasts may become less useful for inventory planning.
Monitoring this type of change often requires delayed feedback. A fraud model may generate a prediction immediately, while the team only learns whether that prediction was correct days or weeks later. MLOps engineers help create the pipelines that connect those real-world outcomes back to the model’s performance metrics.
Teams also need clear thresholds for deciding what happens next. A warning may trigger an investigation, a retraining workflow, a rollback to a previous model, or a temporary switch to a rules-based process.
DevOps monitoring tells teams whether the system is running. MLOps monitoring also tells them whether the model is still performing the job it was deployed to do. Both views are necessary when machine learning becomes part of a business-critical product or workflow.
6. Software Deployment vs. Model Deployment
DevOps and MLOps teams may use similar deployment strategies, including containers, canary releases, blue-green deployments, and automated rollbacks. However, releasing a machine learning model introduces additional decisions about which model should serve predictions, how its outputs will be evaluated, and what happens when its quality changes.
In a conventional DevOps deployment, a team releases a new version of an application or service. Automated checks confirm that the software starts correctly, communicates with connected systems, and remains within expected performance limits. If the release causes errors or instability, the team can redirect traffic to the previous version.
MLOps deployment must coordinate several connected components:
- The approved model version
- Feature transformation logic
- Model-serving code
- Runtime dependencies
- Input and output schemas
- Infrastructure for batch or real-time inference
- Monitoring and feedback pipelines
- Model evaluation thresholds
A model may pass offline testing and still behave differently when exposed to real production data. MLOps teams therefore use controlled release strategies to understand how a new model performs before making it the default.
Common MLOps deployment strategies include:
- Shadow deployment: The new model receives production inputs and generates predictions, but users continue receiving results from the current model. Teams can evaluate the new version without directly affecting the customer experience.
- Canary deployment: A small percentage of production traffic goes to the new model. The percentage increases gradually when its technical and model-performance metrics remain acceptable.
- Champion-challenger testing: The current production model acts as the champion while one or more challenger models compete against it using predefined evaluation criteria.
- A/B testing: Different groups of users receive predictions from different models so the company can measure their effect on a business outcome.
- Blue-green deployment: Two production environments run in parallel, allowing the team to switch traffic between the existing and updated model systems.
The right strategy depends on the use case. A content recommendation model may be suitable for an A/B test because the company can measure engagement across user groups. A fraud-detection or credit-risk model may require stricter approval gates, smaller traffic allocations, and additional human review.
Rollback also works differently in MLOps. Restoring a previous application version may resolve a conventional software regression. A model rollback may require the team to restore the previous model, feature definitions, preprocessing logic, serving configuration, and related dependencies.
A technically successful deployment isn’t automatically a successful model release. The new endpoint may remain stable while the model produces less accurate predictions, increases inference costs, or performs poorly for an important customer segment.
MLOps teams therefore define rollback criteria using both operational and model-level signals. These may include:
- Increased application errors or latency
- Lower prediction accuracy
- Unexpected prediction distributions
- Higher infrastructure or inference costs
- Failed data-quality checks
- Business metrics falling below an agreed threshold
- Fairness, security, or compliance concerns
This is another area where MLOps and DevOps engineers frequently collaborate. DevOps may manage the shared infrastructure, traffic routing, container platform, and release automation. MLOps takes ownership of model evaluation, registry controls, prediction monitoring, and model-specific rollback requirements.
DevOps deployment asks whether the new software version can run reliably. MLOps deployment also asks whether the new model should be trusted to influence production decisions.
7. General Cloud Infrastructure vs. ML Compute and Model Serving
DevOps and MLOps engineers both work with cloud platforms, containers, orchestration tools, networking, storage, and infrastructure as code. Their infrastructure priorities differ because machine learning workloads introduce demanding training jobs, specialized hardware, large datasets, and multiple ways to serve predictions.
A DevOps engineer may design infrastructure for web applications, APIs, databases, background jobs, and internal services. Their goal is to create environments that are secure, scalable, observable, and consistent across development and production.
Common DevOps infrastructure responsibilities include:
- Provisioning cloud resources
- Managing networks and access controls
- Configuring containers and Kubernetes clusters
- Automating environments with infrastructure as code
- Scaling application services
- Managing secrets and configuration
- Improving availability and disaster recovery
- Monitoring infrastructure performance and costs
MLOps engineers build on those foundations while addressing the compute and serving requirements of machine learning systems.
Training a large model may require GPUs, distributed processing, high-capacity storage, and temporary compute clusters. Once the model is trained, the production environment may need a very different setup to deliver predictions quickly and economically.
Training and inference are separate workloads with different performance and cost profiles. Training is often resource-intensive and periodic, while inference may need to respond continuously to thousands or millions of requests.
MLOps infrastructure may include:
- CPU and GPU resource allocation
- Distributed model training
- Training-job orchestration
- Feature stores and data-processing systems
- Model registries and artifact storage
- Batch prediction pipelines
- Real-time model-serving endpoints
- Autoscaling inference infrastructure
- Online and offline feature consistency
- Monitoring for latency, throughput, and cost per prediction
The serving architecture also depends on how the company uses the model. A forecasting system may generate predictions in a scheduled batch each night. A fraud-detection model may need to return a decision within milliseconds. A generative AI application may need to handle variable request lengths, expensive inference calls, and sudden traffic spikes.
These use cases require different design choices.
A batch pipeline can prioritize throughput and cost efficiency. A real-time endpoint must balance response time, availability, resource usage, and prediction quality. Some systems may also need edge deployment, private infrastructure, or regional data processing to meet product and compliance requirements.
MLOps engineers frequently look for ways to control infrastructure expenses by:
- Scheduling training jobs only when needed
- Selecting the appropriate instance type
- Using autoscaling for prediction traffic
- Optimizing model size and serving performance
- Separating experimental workloads from production systems
- Tracking GPU utilization
- Monitoring cost per training run and prediction
- Choosing between managed services and custom infrastructure
For example, a model-serving endpoint may technically scale by adding more GPUs. That approach can become expensive when traffic is uneven or the model uses only a fraction of the available capacity. An MLOps engineer may improve the serving configuration, batch compatible requests, reduce model size, or introduce autoscaling rules that better match actual demand.
The responsibilities often overlap with DevOps, platform engineering, and cloud architecture. A DevOps engineer may manage the shared Kubernetes environment, networking, security policies, and deployment standards. The MLOps engineer then configures how training jobs, model artifacts, feature pipelines, and inference services operate within that environment.
DevOps infrastructure keeps applications available and scalable. MLOps infrastructure extends that foundation to support the complete path from computationally intensive training to reliable, cost-efficient predictions in production.
8. Software Governance vs. Model Governance and Reproducibility
DevOps governance helps teams control how software changes move through development and production. It may include access permissions, security reviews, approval workflows, deployment records, audit logs, and policies for managing infrastructure.
MLOps requires those same controls while extending governance to the data, experiments, and models behind each production prediction.
A company may need to answer questions such as:
- Which dataset was used to train this model?
- Who approved the model for production?
- Which features and parameters influenced the training run?
- How did the model perform before deployment?
- Which model version generated a specific prediction?
- Can the team reproduce the same result?
- Who has access to sensitive training data?
- Why was the model retrained, replaced, or rolled back?
A model is difficult to govern when its lineage ends at the final model file. Teams need a documented connection between the model, its training data, source code, feature logic, experiment settings, evaluation results, and production environment.
This process is known as model lineage. It gives engineering, data science, security, and compliance teams a traceable record of how a model was created and how it changed over time.
Reproducibility is closely related. If two engineers run the same training process with the same inputs, they should be able to produce a consistent result or understand why the results differ. That may require controlling:
- Data versions
- Code versions
- Model configurations
- Random seeds
- Software dependencies
- Training environments
- Hardware settings
- Feature definitions
Reproducibility becomes especially important when a company needs to investigate an unexpected prediction, validate a previous experiment, or demonstrate how a model used in a sensitive workflow was developed.
For example, a financial services company may need to review the model version used during a specific period, the data available at the time, the evaluation criteria it passed, and the person who approved its release. A simple deployment history won’t provide that full record.
MLOps governance may therefore include:
- Model and dataset lineage
- Role-based access controls
- Model approval workflows
- Experiment and evaluation records
- Documentation of intended model use
- Audit trails for production changes
- Data retention policies
- Security and privacy checks
- Explainability requirements
- Processes for retiring outdated models
Not every ML application needs the same level of governance. An internal recommendation tool may follow a lighter approval process than a model involved in lending, healthcare, hiring, insurance, or fraud prevention. The controls should reflect the potential impact of the model’s decisions and the company’s regulatory environment.
DevOps and MLOps teams often share responsibility here. DevOps may manage identity, infrastructure security, secrets, deployment permissions, and system-level audit logs. MLOps adds the controls required to trace data, compare model versions, document evaluations, and manage approvals throughout the machine learning lifecycle.
DevOps governance records how software and infrastructure changed. MLOps governance must also explain how a model was created, why it was approved, and which version influenced a production outcome.
What MLOps and DevOps Have in Common
The differences between MLOps and DevOps become clearer once models, data, and retraining enter the picture. Still, the two disciplines share a substantial technical foundation, and that shared ground is often what allows their teams to collaborate so closely.
Both practices aim to replace fragile manual processes with repeatable, observable systems. Whether a team is releasing an application or deploying a machine learning model, the goal is to move changes into production safely, maintain reliable environments, and respond quickly when something goes wrong.
Their shared practices commonly include:
- Version control
- CI/CD automation
- Infrastructure as code
- Cloud infrastructure
- Containers and orchestration
- Automated testing
- Monitoring and alerting
- Access controls and security
- Incident response
- Cost and performance optimization
- Collaboration across technical teams
Both MLOps and DevOps engineers may work with Git, Docker, Kubernetes, Terraform, cloud platforms, monitoring tools, and automated deployment workflows. The same Kubernetes cluster, for example, might host customer-facing applications managed by DevOps and model-serving endpoints supported by MLOps.
They also share a strong focus on consistency. Development, testing, staging, and production environments should behave predictably so teams can identify problems before a release affects customers. Automation helps reduce configuration errors, while monitoring gives engineers a clearer view of what happens after deployment.
Security is another shared responsibility. Both disciplines may help manage permissions, secrets, network controls, software dependencies, audit logs, and vulnerability checks. MLOps expands that scope to include model artifacts and training data, but it still relies on many of the security standards established across the broader engineering platform.
The main distinction lies in what each workflow is designed to protect.
DevOps protects the reliability of software applications, infrastructure, and delivery processes. MLOps extends those practices to protect the reliability of training pipelines, model versions, data inputs, and production predictions.
That relationship is why MLOps is often described as an extension of DevOps for machine learning systems. However, the titles aren’t automatically interchangeable. A DevOps engineer may have deep experience in infrastructure and deployments without having worked with model evaluation, experiment tracking, or drift detection. An MLOps engineer may specialize in ML platforms while having a narrower background in broader application operations.
The engineer’s actual production experience matters more than the label on their résumé. When hiring, companies should evaluate which systems the person has owned, what types of failures they’ve handled, and whether their expertise matches the lifecycle the business needs to support.
How DevOps and MLOps Teams Work Together
MLOps and DevOps rarely operate in completely separate environments. A production AI feature still depends on applications, APIs, cloud infrastructure, security controls, databases, and monitoring systems. The model may be the most visible part of the workflow, but it can only deliver value when the surrounding software and infrastructure work reliably.
In many companies, DevOps engineers manage the shared technology platform while MLOps engineers manage the systems specific to the machine learning lifecycle.
A typical workflow may look like this:
- A data scientist develops and evaluates a model. They explore data, create features, test algorithms, and select an approach that meets the project’s performance goals.
- An ML engineer prepares the model for production. They may improve the training code, build the inference service, optimize performance, and integrate the model with the company’s application.
- An MLOps engineer creates the model lifecycle workflows. They automate training, validation, model registration, deployment, monitoring, and retraining so the process can be repeated reliably.
- A DevOps or platform engineer provides the shared production foundation. They may manage cloud accounts, networking, Kubernetes clusters, access controls, secrets, CI/CD standards, observability, and disaster recovery.
- Both teams monitor the production system. DevOps watches application and infrastructure health, while MLOps tracks data quality, model behavior, prediction performance, and retraining signals.
Consider an e-commerce company launching a product recommendation feature. The DevOps team may provision the cloud environment, configure traffic routing, manage the application deployment pipeline, and set alerts for latency or service failures. The MLOps team may build the feature pipeline, register model versions, deploy the recommendation model, and monitor whether its predictions continue improving engagement.
When the recommendation service slows down, DevOps may investigate networking, containers, resource limits, or application dependencies. When recommendations become less relevant despite normal service performance, MLOps may investigate data drift, feature quality, training data, or model degradation.
Some responsibilities naturally overlap, including:
- Container and Kubernetes configuration
- Cloud resource provisioning
- Deployment automation
- Service-level monitoring
- Access controls
- Incident response
- Infrastructure costs
- Production release planning
Clear ownership prevents these shared areas from becoming gaps. Teams should agree on who maintains each pipeline, who receives each alert, who approves model releases, and who leads the response when a production problem crosses application and model boundaries.
For example, an inference endpoint may experience higher latency after a model update. The underlying cause could be the model’s size, the serving configuration, insufficient compute, or a problem elsewhere in the application. Resolving it may require the MLOps engineer’s understanding of model behavior and the DevOps engineer’s knowledge of the production platform.
The strongest setup creates shared standards without forcing both roles to own everything. DevOps establishes reliable infrastructure and delivery practices across the organization. MLOps builds on that foundation to manage the additional dependencies, risks, and feedback loops introduced by machine learning.
This collaboration becomes more important as the number of models, engineering teams, and production use cases grows. Well-defined boundaries help companies avoid duplicate tools, disconnected monitoring systems, and unclear incident ownership while giving each engineer space to focus on their area of expertise.
MLOps Engineer vs. DevOps Engineer: Who Owns What?
The boundary between MLOps and DevOps changes from one company to another. A startup may ask one infrastructure engineer to manage both application deployments and model-serving systems, while a larger company may divide the work across DevOps, platform engineering, data engineering, ML engineering, and MLOps teams.
Even so, the responsibility split usually follows a clear pattern: DevOps owns the broader software and infrastructure platform, while MLOps owns the processes that keep machine learning models reproducible, deployable, and reliable in production.
The table shows typical ownership rather than rigid job boundaries. A DevOps engineer with strong machine learning infrastructure experience may manage model deployment and GPU environments. An MLOps engineer with a platform engineering background may own Kubernetes, Terraform, and broader cloud architecture.
The distinction becomes most useful when assigning accountability.
For example:
- A failed application deployment usually falls under DevOps.
- A failed model-training job usually falls under MLOps.
- An unavailable Kubernetes cluster may require DevOps or platform engineering.
- A healthy endpoint producing weaker predictions may require MLOps.
- Increased inference latency after a model release may require both teams.
- Unauthorized access to training data may involve MLOps, DevOps, security, and data engineering.
Companies should assign ownership based on the system, alert, and decision involved, not the job title alone. Every critical workflow should have a clear owner, including who maintains it, who responds when it fails, and who approves changes before they reach production.
This clarity becomes particularly important during hiring. Before opening an MLOps or DevOps position, define whether the new engineer will primarily own application delivery, shared infrastructure, model lifecycle automation, or a combination of those areas. That scope will determine which background and technical experience the role requires.
Which Engineer Should Your Company Hire?
Choosing between an MLOps engineer and a DevOps engineer starts with one practical question: which part of your production workflow needs dedicated ownership?
A DevOps engineer is usually the stronger fit when the bottleneck sits in software delivery, cloud infrastructure, reliability, or deployment automation. An MLOps engineer becomes more valuable when the company needs to move models into production, monitor their behavior, and manage the full machine learning lifecycle.
Some companies need both roles because their AI products depend on a reliable software platform and a specialized model operations layer.
Hire a DevOps Engineer When
A DevOps engineer can help when your engineering team needs a stronger foundation for building, releasing, and operating software.
Common signals include:
- Software deployments depend on manual steps
- Releases frequently cause downtime or regressions
- Developers spend too much time managing infrastructure
- Development, staging, and production environments behave differently
- Cloud resources are difficult to scale or control
- Monitoring and incident response are inconsistent
- CI/CD pipelines are slow, fragile, or incomplete
- Security permissions and infrastructure access need clearer management
DevOps is also often the right first hire when a company uses AI through third-party APIs rather than training and operating its own models. In that setup, the main challenge is usually integrating the AI service into a reliable application rather than managing a custom machine learning pipeline.
For example, a SaaS company adding a generative AI assistant through an external API may need DevOps support for application deployment, traffic management, observability, secrets, and cloud costs. It may have little need for continuous training, model registries, or data drift monitoring.
Choose DevOps when the primary problem is getting software and infrastructure to operate reliably at scale.
Hire an MLOps Engineer When
An MLOps engineer is a stronger fit when the company already trains, fine-tunes, or deploys its own machine learning models and needs a repeatable way to manage them in production.
Common signals include:
- Models remain in notebooks because deployment is difficult
- Data scientists maintain production infrastructure themselves
- Model releases depend on manual handoffs
- Training jobs are difficult to reproduce
- Teams can’t easily trace a model back to its data and code
- Production models lack drift or performance monitoring
- Retraining happens inconsistently
- Several model versions are difficult to manage
- GPU usage or inference costs are increasing
- Model approvals and governance need clearer controls
An MLOps engineer can connect the work of data scientists, ML engineers, data engineers, and infrastructure teams. They build the systems that turn individual experiments into dependable production workflows.
Consider a fintech company running a custom fraud-detection model. The service may already be deployed, but the team could struggle to track model versions, validate new training data, monitor prediction quality, and retrain the model safely. Those problems point toward MLOps rather than general DevOps.
Companies can review what an MLOps engineer does to understand the role’s deeper responsibilities before defining the position.
Choose MLOps when the main challenge is keeping models deployable, traceable, observable, and adaptable after they reach production.
Hire Both When
Many production AI systems need DevOps and MLOps expertise because the model operates inside a broader software product.
Hiring both may make sense when:
- Machine learning features are central to the product
- Several teams share cloud infrastructure
- The company operates multiple production models
- Models require frequent training or deployment
- AI workloads must meet strict reliability standards
- The system handles sensitive or regulated data
- Model-serving infrastructure must scale with customer traffic
- Application incidents and model-quality incidents require different expertise
In this structure, DevOps may own the shared cloud platform, deployment standards, networking, access controls, and application observability. MLOps may own training pipelines, experiment tracking, model registries, validation, model releases, drift monitoring, and retraining.
For example, a marketplace with an AI-powered search engine may need DevOps engineers to maintain the customer-facing application and shared infrastructure. MLOps engineers may manage the ranking models, feature pipelines, evaluation process, and model-serving system.
The roles can also sit within broader platform or machine learning infrastructure teams. The exact reporting structure matters less than ensuring that both the software lifecycle and the model lifecycle have clear owners.
Use the Bottleneck to Make the Decision
Job titles can become confusing because DevOps, platform engineering, ML engineering, and MLOps responsibilities often overlap. A clearer approach is to identify where work slows down or risk accumulates.
The best choice reflects the systems your company already operates and the next constraint it needs to remove. Defining that constraint before writing the job description will lead to a clearer scope, more relevant candidates, and a stronger technical interview process.
Can One Engineer Handle Both DevOps and MLOps?
One engineer can sometimes manage both DevOps and MLOps responsibilities, especially when the company has a small technical team, a limited number of models, and a relatively simple production environment.
The overlap between the roles makes this possible. Both may work with cloud platforms, Kubernetes, Docker, Terraform, CI/CD pipelines, monitoring systems, security controls, and production incident response. A DevOps engineer with machine learning infrastructure experience may be able to build model deployment pipelines, manage GPU resources, and support retraining workflows. An MLOps engineer with a strong platform background may also maintain shared infrastructure and application delivery systems.
The feasibility of a combined role depends more on operational complexity than company size alone.
A hybrid DevOps and MLOps engineer may be enough when:
- The company operates one or two production models
- Model updates happen infrequently
- Training pipelines use managed cloud services
- The ML workload supports an internal tool or noncritical feature
- Application infrastructure is relatively straightforward
- Data scientists and ML engineers can handle part of the model workflow
- Compliance and governance requirements are limited
- Production incidents are uncommon and easy to diagnose
For example, an early-stage SaaS company may use a custom classification model inside one product feature. A senior infrastructure engineer with experience in model serving could manage the application pipeline, deploy the model endpoint, monitor basic performance, and coordinate occasional retraining.
That arrangement can keep ownership clear while the system remains manageable.
When a Combined Role Starts Becoming Risky
A single engineer may struggle as the software platform and machine learning lifecycle become more demanding.
Specialization becomes more valuable when:
- Several models run across different products or teams
- Models require frequent retraining
- Real-time inference must meet strict latency targets
- GPU costs require ongoing optimization
- Model drift could affect revenue or customer decisions
- Training data contains sensitive information
- The company needs detailed model lineage and approval records
- Application and model incidents happen at the same time
- Multiple teams depend on shared ML infrastructure
- The engineer spends most of their time responding to operational issues
The challenge is usually capacity rather than technical ability. One experienced engineer may understand both areas but still lack enough time to maintain application reliability, improve infrastructure, support data scientists, investigate drift, manage model releases, and respond to incidents.
Combining the roles works best when the responsibilities form one manageable system. It becomes fragile when one person is expected to operate two growing platforms.
Can a DevOps Engineer Do MLOps?
A DevOps engineer can transition into MLOps because they already understand many of the production foundations involved. Experience with cloud infrastructure, containers, automation, observability, security, and CI/CD provides a strong starting point.
They’ll also need practical knowledge of:
- Machine learning development workflows
- Python and ML frameworks
- Data pipelines and feature engineering
- Experiment tracking
- Model registries
- Data and model versioning
- Model evaluation
- Batch and real-time inference
- Drift detection
- Continuous training
- Model governance
The transition is usually smoother when the engineer has already supported data science teams or deployed machine learning services. Infrastructure experience alone may cover the serving environment, while the full MLOps role also requires understanding how data and model quality influence production outcomes.
Can an MLOps Engineer Do DevOps?
Some MLOps engineers can handle broader DevOps work, particularly when they started in software engineering, cloud infrastructure, platform engineering, or site reliability engineering.
Others specialize more heavily in ML pipelines, model tooling, data workflows, or experiment management. They may be comfortable deploying model services while having less experience with organization-wide networking, database reliability, disaster recovery, or large application platforms.
That’s why companies should examine the candidate’s previous ownership rather than assuming capability from the title.
Ask what they have personally built and operated:
- Have they managed shared cloud infrastructure?
- Have they owned application CI/CD pipelines?
- Have they supported production Kubernetes clusters?
- Have they created training and retraining workflows?
- Have they deployed and monitored models?
- Have they led incidents involving applications, infrastructure, or model quality?
The answers reveal whether the person can realistically cover both lifecycles.
Define the Role Around Outcomes
A hybrid job description should clearly state which outcomes take priority.
For example, the role might focus on:
- Building shared cloud infrastructure for applications and ML workloads
- Automating software and model deployments
- Establishing monitoring for services and model behavior
- Supporting data scientists as models move into production
- Improving reliability and controlling cloud costs
Avoid combining two complete job descriptions into one position. Prioritize the systems the engineer will own during their first six to twelve months and identify which responsibilities other team members will continue handling.
One engineer can cover DevOps and MLOps when the scope is focused, the environment is manageable, and the candidate has proven experience across both areas. As the platform grows, separating ownership usually gives each lifecycle the attention it needs.
MLOps Engineer vs. DevOps Engineer Salary
MLOps and DevOps salaries often overlap because both roles require experience with cloud platforms, automation, containers, infrastructure, monitoring, and production reliability. Compensation usually depends on the engineer’s seniority, location, technical scope, and the complexity of the systems they’ll own.
An MLOps engineer may command a higher salary when the position requires specialized experience with GPU infrastructure, distributed training, model-serving architecture, experiment tracking, continuous training, or machine learning governance. These skills sit at the intersection of software engineering, data engineering, cloud infrastructure, and machine learning, which can make experienced candidates harder to find.
DevOps salaries can be equally competitive when the role includes responsibility for:
- Large cloud environments
- High-availability systems
- Kubernetes platform architecture
- Security and compliance
- Site reliability engineering
- Complex infrastructure migrations
- Organization-wide developer platforms
The job title alone doesn’t determine which engineer costs more. A senior DevOps engineer responsible for a business-critical platform may earn more than an MLOps engineer supporting a limited number of internal models. A senior MLOps engineer designing infrastructure for large-scale training and real-time inference may earn more than a DevOps engineer with a narrower deployment scope.
Companies should evaluate compensation based on:
- Years of relevant production experience
- Seniority and expected autonomy
- Cloud and infrastructure complexity
- Number and type of production systems
- Batch versus real-time workloads
- GPU and distributed computing requirements
- Security and governance expectations
- Incident response responsibilities
- Leadership or architecture ownership
- Local hiring market
Location also has a significant effect on compensation. U.S. companies can often access experienced engineers in Latin America at a lower total salary than comparable U.S.-based hires while still working within overlapping time zones.
For a detailed breakdown of compensation by seniority and region, see South’s guide to MLOps engineer salaries in the U.S. and Latin America. Keeping the full benchmarks in one dedicated resource helps ensure the figures remain consistent as hiring markets change.
The better hiring decision comes from matching compensation to ownership rather than choosing a title based on its average salary. Define the production systems, risks, and outcomes the engineer will manage before setting the budget for the role.
Hire an MLOps or DevOps Engineer From Latin America With South
Once you know whether your company needs DevOps, MLOps, or a blend of both, South can help you find an engineer with the right production experience.
South connects U.S. companies with pre-vetted professionals across Latin America who can support cloud infrastructure, CI/CD, Kubernetes, model deployment, ML pipelines, monitoring, and production reliability. The search is shaped around the systems the engineer will own, the problems they need to solve, and the level of autonomy your team expects.
Hiring through South also gives your team access to professionals who work in overlapping U.S. time zones, making it easier to coordinate deployments, investigate incidents, and collaborate with engineering, data science, security, and product teams in real time.
Whether you need a DevOps engineer to strengthen application delivery or an MLOps engineer to move models into production, South can help you:
- Define the role around your current bottleneck
- Identify the right level of seniority
- Source candidates with relevant cloud and production experience
- Evaluate technical fit for your infrastructure and workflows
- Build a shortlist aligned with your team and working style
The right hire should understand more than the tools in your stack. They should know how to build reliable systems, respond when they fail, and improve the lifecycle your business depends on.
Schedule a call with South to find MLOps or DevOps engineers in Latin America whose experience matches your production environment.
Frequently Asked Questions (FAQs)
Is MLOps Part of DevOps?
MLOps applies many DevOps principles to machine learning systems, including automation, version control, CI/CD, infrastructure management, and monitoring. It also adds processes for data validation, experiment tracking, model evaluation, continuous training, and model governance.
Does MLOps Replace DevOps?
MLOps doesn’t replace DevOps. Most AI products still depend on applications, APIs, cloud infrastructure, security controls, and deployment systems supported by DevOps. MLOps manages the additional lifecycle required to train, release, monitor, and update machine learning models.
Can a DevOps Engineer Work in MLOps?
A DevOps engineer can move into MLOps with additional experience in machine learning workflows, data pipelines, model serving, experiment tracking, drift detection, and continuous training. Their background in cloud infrastructure, containers, automation, and production reliability provides a strong foundation.
What’s the Difference Between CI/CD in DevOps and MLOps?
DevOps CI/CD automates how software changes are tested and deployed. MLOps expands those pipelines to include data validation, model evaluation, model registration, and controlled model releases. It may also add continuous training, allowing models to be retrained when new data arrives or performance changes.
Who Is Responsible for Model Monitoring?
MLOps engineers typically own model-level monitoring, including data drift, prediction distributions, model accuracy, and retraining signals. DevOps or platform engineers may manage the underlying observability tools, infrastructure health, logs, latency, and service availability.
Do Small Companies Need an MLOps Engineer?
A small company may not need a dedicated MLOps engineer when it operates a limited number of models, relies on managed services, and updates models infrequently. Dedicated MLOps expertise becomes more valuable as deployments, monitoring, retraining, governance, and infrastructure requirements grow.
Is MLOps More Difficult Than DevOps?
Neither discipline is universally more difficult. DevOps may involve complex cloud platforms, distributed systems, security, and high-availability infrastructure. MLOps adds challenges related to data quality, model behavior, reproducibility, drift, and statistical evaluation. The difficulty depends on the systems and responsibilities involved.
What Is the Difference Between MLOps and ML Engineering?
ML engineers typically focus on developing, optimizing, and integrating machine learning models. MLOps engineers focus more heavily on the infrastructure and automated processes used to train, deploy, monitor, govern, and update those models in production. Responsibilities can overlap depending on the company.
Is LLMOps the Same as MLOps?
LLMOps is a specialized area within the broader MLOps field focused on large language model applications. It may include prompt management, model and provider selection, evaluation, retrieval pipelines, inference costs, latency, safety controls, and monitoring for generated outputs.
Should You Hire an MLOps Engineer or a DevOps Engineer First?
Hire a DevOps engineer first when your main challenges involve software delivery, cloud infrastructure, application reliability, or observability. Hire an MLOps engineer when your company already develops custom models and needs better training, deployment, monitoring, lineage, or retraining workflows. Companies operating complex AI products may need both.
