MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining machine learning models in production. It applies DevOps principles -- automation, CI/CD, monitoring, infrastructure as code -- to the machine learning lifecycle. MLOps engineers ensure that ML models work reliably at scale, not just in a data scientist's notebook.

Is MLOps part of DevOps?

MLOps is an extension of DevOps, not a separate discipline. Approximately 70% of MLOps work uses standard DevOps tools and practices -- Kubernetes, Docker, CI/CD pipelines, Terraform, monitoring. The additional 30% involves ML-specific concerns like model versioning, data pipelines, training orchestration, and drift detection.

Do I need ML knowledge for MLOps?

You need a basic understanding of how ML models work (training, inference, evaluation metrics) but not deep ML expertise. Most MLOps work is infrastructure -- building pipelines, managing Kubernetes clusters, optimising GPU resources, and automating deployments. A strong DevOps background is more valuable than a strong ML background for most MLOps roles.

MLOps salary vs DevOps salary: which pays more?

MLOps roles typically pay 10-25% more than equivalent DevOps roles due to higher demand and lower supply. In the UK, mid-level MLOps engineers earn £65,000-£100,000 compared to £55,000-£85,000 for DevOps. In the US, MLOps ranges from $120,000-$180,000 compared to $100,000-$155,000 for DevOps. Senior ML infrastructure roles at AI companies can exceed £140,000 / $220,000.

MLOps vs DevOps: What's the Difference and How They Connect

MLOps is DevOps for machine learning. That single sentence captures the core relationship between these two disciplines. If you understand DevOps, you already understand roughly 70% of MLOps. The remaining 30% is ML-specific tooling and concepts layered on top of the same foundation.

This is not a minor detail. It means that DevOps engineers are the most natural candidates for MLOps roles -- and that the transition from DevOps to MLOps is one of the most efficient career moves in tech right now. AI companies need people who can deploy, scale, and monitor ML systems in production. Those are infrastructure skills, not machine learning skills.

Here is how the two disciplines connect, where they diverge, and what this means for your career.

The side-by-side mapping

Every core DevOps concept has a direct equivalent in MLOps. The table below shows the mapping:

DevOps Concept	MLOps Equivalent	What changes
Source code	Model code + training data	Two artefacts to version instead of one
Build	Train	Compiling becomes training (minutes → hours/days)
Unit tests	Model validation	Testing accuracy, bias, and performance instead of logic
Artefact (Docker image)	Model artefact (serialised model)	Different packaging format, same concept
Deploy	Serve	Containers serving predictions instead of web pages
Monitor	Monitor + drift detection	Standard metrics plus model-specific metrics
CI/CD pipeline	ML pipeline	Same automation, additional stages
Infrastructure (Terraform)	Infrastructure + GPU resources	Same IaC, more expensive hardware
Rollback	Model rollback	Same concept, different triggers

If you squint, MLOps is DevOps with different nouns. The verbs -- automate, deploy, monitor, scale, optimise -- are identical.

Where DevOps and MLOps overlap (the 70%)

The majority of MLOps work uses standard DevOps tools and practices. Here is what carries over directly:

Containerisation

ML models run in Docker containers, just like any other application. The data scientist exports a model. The MLOps engineer packages it in a container with a serving framework (TensorFlow Serving, Triton Inference Server, or a custom Flask/FastAPI wrapper). The container goes into a registry. Kubernetes runs it.

If you know Docker, you know how to containerise an ML model. The Dockerfile might look slightly different -- it installs PyTorch instead of Node.js -- but the process is identical.

Kubernetes orchestration

Production ML systems run on Kubernetes. Pods serve inference requests. Horizontal Pod Autoscalers handle traffic spikes. Services route requests. Ingress controls external access.

The Kubernetes layer for ML models is the same Kubernetes you already know, with one addition: GPU scheduling. Kubernetes can schedule pods onto GPU nodes using NVIDIA device plugins and resource requests:

resources:
  limits:
    nvidia.com/gpu: 1

This tells Kubernetes the pod needs one GPU. Everything else -- deployments, services, scaling, monitoring -- is standard Kubernetes.

CI/CD pipelines

ML models need automated pipelines just like application code. When a data scientist pushes new model code or training data, a pipeline should:

Validate the data
Train the model
Evaluate the model against benchmarks
Package the model in a container
Deploy to staging
Run integration tests
Deploy to production (canary or blue-green)

Steps 3 through 7 are identical to any CI/CD pipeline. Steps 1 and 2 are ML-specific. The tooling (GitHub Actions, GitLab CI, ArgoCD) is the same.

Infrastructure as Code

ML infrastructure is provisioned with Terraform, just like any other infrastructure. GPU instances, networking, storage, IAM permissions, monitoring -- all defined in .tf files. The resources are more expensive (GPU instances cost 10-50x more than CPU instances), which makes IaC even more critical. You need reproducibility and cost tracking.

Monitoring and observability

Prometheus, Grafana, alerting rules, dashboards -- all the same tools. ML monitoring adds additional metrics (model accuracy, prediction latency, input data distributions), but these are custom Prometheus metrics collected the same way as any application metric.

Networking and security

VPCs, security groups, IAM roles, secrets management, TLS certificates -- all identical. ML systems have the same networking and security requirements as any production system.

Where MLOps differs (the 30%)

The ML-specific layer adds concepts that do not exist in traditional DevOps:

Data versioning and management

In DevOps, you version code. In MLOps, you also version data. A model's behaviour depends on the data it was trained on. If the training data changes, the model changes. You need to track which data produced which model.

Tools like DVC (Data Version Control) and LakeFS handle this. They work alongside Git -- Git versions the code, DVC versions the data. The concept is straightforward, but the scale can be enormous (terabytes of training data).

Model versioning and registries

Application code produces Docker images stored in container registries. Model training produces model artefacts stored in model registries.

A model registry tracks:

Which version of the model is in production
Training metrics for each version (accuracy, loss, etc.)
Which data and code produced each version
Who approved each version for deployment

Tools like MLflow, Weights & Biases, and cloud-native registries (SageMaker Model Registry, Vertex AI Model Registry) handle this. Conceptually, it is a container registry with additional metadata.

Training orchestration

Training a model is not like building a Docker image. It can take hours or days, requires GPU clusters, and involves hyperparameter tuning (running the same training process dozens of times with different settings to find the best configuration).

Training orchestration tools (Kubeflow, SageMaker, Vertex AI Pipelines) manage this. They schedule training jobs on GPU clusters, track experiments, and manage the training lifecycle. This is genuinely new -- there is no direct DevOps equivalent.

Model drift detection

This is the most ML-specific concept. After deployment, a model's accuracy can degrade over time because the real-world data it encounters starts differing from its training data. This is called drift.

Example: A fraud detection model trained on 2024 data starts seeing new types of fraud in 2026 that it was not trained on. Its accuracy drops. Drift detection monitors input data distributions and prediction patterns to catch this degradation early.

DevOps has monitoring and alerting. MLOps extends this with statistical monitoring of data distributions and model outputs. The tools are different (Evidently AI, WhyLabs, custom Prometheus metrics), but the principle -- "detect problems before users notice" -- is pure DevOps thinking.

Feature stores

A feature store is a centralised repository of prepared data features (inputs to ML models). It ensures that the features used during training match the features used during inference. This prevents a common bug called training-serving skew.

Feature stores (Feast, Tecton, cloud-native options) are ML-specific infrastructure. There is no direct DevOps equivalent, but the operational management -- deploying, scaling, monitoring the feature store -- is standard infrastructure work.

The career transition: DevOps to MLOps

DevOps engineers are the highest-demand hires for MLOps roles. Here is why:

What companies actually need

When a company hires for an "MLOps Engineer," they typically need someone who can:

Build and maintain Kubernetes clusters for model serving
Create CI/CD pipelines for model deployment
Manage GPU infrastructure with Terraform
Set up monitoring and alerting for production models
Optimise cloud costs for GPU workloads
Automate the model deployment lifecycle

Items 1-6 are DevOps skills applied to ML workloads. A DevOps engineer with basic ML understanding can do all of them. A data scientist with no infrastructure experience cannot.

The knowledge gap is small

A DevOps engineer transitioning to MLOps needs to learn:

Basic ML concepts -- What training, inference, and evaluation mean (1-2 weeks of study)
Model serving frameworks -- TensorFlow Serving, Triton, or similar (1 week)
GPU management -- NVIDIA device plugins, GPU scheduling in Kubernetes (1 week)
ML pipeline tools -- Kubeflow, MLflow, or equivalent (2 weeks)
Drift detection basics -- What it is and how to monitor for it (1 week)

That is 6-8 weeks of additional learning on top of a solid DevOps foundation. Compare this to a data scientist learning Kubernetes, Terraform, CI/CD, cloud networking, and monitoring from scratch -- that is 4-6 months.

The DevOps → MLOps transition is dramatically more efficient than any other path into MLOps.

The salary premium

MLOps roles command a premium over equivalent DevOps roles:

Level	DevOps Salary (UK)	MLOps Salary (UK)	Premium
Mid-level	£55,000 £85,000	£65,000 £100,000	+15-20%
Senior	£80,000 £120,000	£90,000 £140,000	+10-15%
Lead/Staff	£100,000 £150,000	£120,000 £180,000	+15-20%

Level	DevOps Salary (US)	MLOps Salary (US)	Premium
Mid-level	$100,000 $155,000	$120,000 $180,000	+15-20%
Senior	$140,000 $200,000	$160,000 $230,000	+10-15%
Lead/Staff	$170,000 $250,000	$200,000 $300,000	+15-20%

The premium exists because there are fewer qualified MLOps engineers than DevOps engineers, and demand from AI companies is growing faster than supply.

When you need MLOps

Not every company needs MLOps. Here is the decision framework:

You need MLOps when:

You have ML models in production serving real users
Multiple data scientists are training and deploying models regularly
You need reproducibility -- the ability to recreate any model version
Model accuracy is business-critical (fraud detection, recommendation engines, pricing)
You spend significant money on GPU infrastructure and need cost control
You need compliance and auditability for model decisions

You do not need MLOps when:

You have one model deployed once and rarely updated
Your ML work is experimental and not yet in production
You are a small team where data scientists manage their own deployments
Your models run as batch jobs on a schedule (simpler orchestration is sufficient)

MLOps makes sense when the scale, complexity, or business criticality of your ML systems justifies the investment in specialised tooling and processes.

The convergence: DevOps and MLOps are merging

A clear trend in 2026: the line between DevOps and MLOps is blurring. As more companies deploy ML models, the expectation is shifting from "DevOps teams that don't touch ML" and "ML teams that don't understand infrastructure" towards integrated platform teams that handle both.

This is why understanding the connection between DevOps and MLOps matters for your career:

DevOps engineers who understand ML concepts are the most versatile and highest-paid infrastructure professionals
The tools are converging -- Kubernetes, Terraform, and CI/CD tools are adding ML-native features
AI companies are the biggest employers of infrastructure engineers, and they need DevOps skills more than ML skills

The smart career move is to build a strong DevOps foundation first, then layer ML-specific knowledge on top. You get the broadest job market (DevOps), the highest-growth niche (MLOps), and the premium salary that comes with both.

For a deeper exploration of AI infrastructure and where DevOps fits, see our complete guide to AI infrastructure. To understand the DevOps foundation that makes MLOps possible, start with what is DevOps.