MLOps (Machine Learning Operations) applies DevOps principles to machine learning systems. It covers the full lifecycle of ML models in production: data management, training, validation, deployment, monitoring, and retraining. MLOps ensures that ML models work reliably at scale, not just in a data scientist's notebook.

Why is MLOps growing so fast?

MLOps is growing because companies are moving ML models from experimentation to production. A 2024 Gartner report found that only 54% of ML models make it to production the rest fail due to operational challenges that MLOps solves. As AI becomes core to business strategy, the demand for engineers who can operationalise ML systems is accelerating at 40%+ year-over-year.

What tools are used in MLOps?

Key MLOps tools include MLflow (experiment tracking and model registry), Kubeflow (ML pipelines on Kubernetes), Weights and Biases (experiment tracking), DVC (data versioning), Feast (feature store), Evidently AI (model monitoring), vLLM (LLM serving), and Triton Inference Server (model serving). These sit alongside standard DevOps tools like Kubernetes, Terraform, and Prometheus.

Can a DevOps engineer become an MLOps engineer?

Yes and it is the most efficient career path into MLOps. Approximately 70% of MLOps work uses standard DevOps tools (Kubernetes, Docker, Terraform, CI/CD, monitoring). A DevOps engineer needs 6-8 weeks of additional learning in ML concepts, model serving, GPU management, and ML-specific tools to transition into an MLOps role.

What does an MLOps engineer earn?

MLOps engineers earn 10-25% more than equivalent DevOps engineers. In the UK, mid-level MLOps engineers earn £65,000-£100,000. In the US, mid-level salaries range from $120,000-$180,000. Senior ML infrastructure roles at AI companies can exceed £140,000 (UK) or $220,000 (US). The premium reflects high demand and limited supply of qualified engineers.

The Rise of MLOps: Where DevOps Meets Machine Learning

MLOps is the fastest-growing specialisation to emerge from DevOps. It applies the same principles that made software delivery reliable automation, CI/CD, monitoring, infrastructure as code to the specific challenges of deploying and maintaining machine learning models in production. And the demand for MLOps engineers is growing at over 40% year-over-year because companies are discovering that building an ML model is the easy part. Running it reliably in production is where most projects fail.

This is not a future trend. It is happening now. Every company deploying AI from startups fine-tuning language models to enterprises running fraud detection systems needs MLOps. And the engineers best positioned to fill these roles are not data scientists. They are DevOps engineers.

Here is what MLOps is, how the lifecycle works, what tools power it, and why your DevOps skills are the most direct path into this high-growth field.

Why ML models need operational discipline

Building a machine learning model that works in a Jupyter notebook is straightforward. Getting that model to serve predictions reliably in production, at scale, 24/7, with consistent accuracy that is an entirely different problem.

Here is what goes wrong when companies skip MLOps:

The "notebook to production" gap. A data scientist trains a model on their laptop. It achieves 95% accuracy on test data. They hand it to the engineering team. The engineering team spends weeks figuring out how to deploy it. Dependencies conflict. The serving framework is different from the training framework. The model runs 10x slower in production than in the notebook. Performance degrades because production data looks different from training data.

The reproducibility problem. Three months later, the model needs retraining. But nobody recorded which version of the training data, which hyperparameters, or which code version produced the current model. Recreating it is guesswork.

The drift problem. The model worked well in January. By June, its accuracy has dropped from 95% to 78%. Real-world data has shifted new patterns, new categories, new user behaviours and the model was never designed to detect or adapt to these changes.

The scale problem. The model handles 100 requests per second in testing. In production, it needs to handle 10,000. Scaling a model serving system across multiple GPUs, with load balancing, auto-scaling, and failover, is an infrastructure challenge, not a machine learning challenge.

These are not ML problems. They are operational problems. And they are exactly the kind of problems that DevOps was built to solve.

The MLOps lifecycle

The MLOps lifecycle mirrors the DevOps lifecycle, with ML-specific stages added. Here is the full loop:

1. Data management

Everything in ML starts with data. The data management stage involves:

Collecting data from production systems, APIs, databases, or third-party sources
Versioning data so you can reproduce any training run (tools: DVC, LakeFS)
Validating data quality checking for missing values, outliers, format issues, and distribution shifts
Storing data efficiently in data lakes or feature stores

In DevOps terms, think of data as source code. Just as code needs version control and quality checks, data needs the same discipline. The difference is scale training datasets can be terabytes or petabytes.

2. Feature engineering

Features are the processed inputs that ML models consume. A feature store (Feast, Tecton) ensures that:

Training features match serving features (preventing training-serving skew)
Features are computed consistently across teams
Feature computation is efficient and cached

This stage has no direct DevOps equivalent, but the infrastructure that runs feature stores Kubernetes, Redis, object storage is standard DevOps territory.

3. Training

Training is where a model learns from data. In MLOps, training is automated and tracked:

Experiment tracking record every training run with its hyperparameters, metrics, and outputs (tools: MLflow, Weights & Biases)
Distributed training for large models, training runs across multiple GPUs or nodes (tools: Kubeflow, Ray)
Hyperparameter tuning automated search for the best model configuration (tools: Optuna, Ray Tune)
GPU scheduling Kubernetes allocates GPU resources to training jobs, ensuring efficient utilisation

In DevOps terms, training is the "build" step. It takes source material (code + data) and produces an artefact (a trained model). The artefact goes into a registry, just like a Docker image.

4. Validation

Before a model reaches production, it must pass validation:

Accuracy benchmarks does the new model meet or exceed the current model's performance?
Bias testing does the model perform equitably across demographic groups?
Latency testing can the model serve predictions within the required time budget?
A/B comparison how does the new model compare to the current production model on real data?

This is the equivalent of CI testing. Automated checks gate the model before deployment, just as unit tests gate code before deployment.

5. Deployment

Deploying a model means making it available to serve predictions:

Containerisation the model and its serving framework are packaged in a Docker container
Serving infrastructure the container runs on Kubernetes, often with GPU nodes
Deployment strategies canary deployments (route 5% of traffic to the new model, monitor, then roll out) or blue-green deployments
Autoscaling the serving infrastructure scales based on request volume

This stage is pure DevOps. Docker, Kubernetes, load balancing, health checks, rolling updates everything transfers directly.

Key serving tools:

vLLM high-performance serving for large language models, with continuous batching and PagedAttention for efficient GPU memory use
Triton Inference Server NVIDIA's model server supporting multiple frameworks (TensorFlow, PyTorch, ONNX) with dynamic batching
TensorFlow Serving production serving for TensorFlow models
Seldon Core Kubernetes-native model serving with A/B testing and canary deployments

6. Monitoring

Production models need continuous monitoring for:

Standard infrastructure metrics latency, throughput, error rates, CPU/GPU utilisation (Prometheus, Grafana)
Model-specific metrics prediction confidence distributions, input data statistics, output distributions
Data drift has the distribution of incoming data shifted compared to training data?
Concept drift have the relationships between inputs and outputs changed?
Feature drift have individual feature distributions changed?

Drift detection is the genuinely new concept for DevOps engineers. Standard monitoring tells you if the system is healthy. Drift detection tells you if the model is still accurate. Tools like Evidently AI, WhyLabs, and Arize handle this by comparing production data distributions against training baselines.

7. Retraining

When monitoring detects degraded performance, the cycle restarts:

Trigger retraining automatically (on schedule or when drift exceeds thresholds)
Pull updated data
Train a new model version
Validate against benchmarks
Deploy via canary
Monitor the new version

This closed loop deploy, monitor, retrain, deploy is the core of mature MLOps. It ensures that models improve over time rather than silently degrading.

The MLOps tool landscape

The MLOps ecosystem has matured rapidly. Here are the key tools organised by function:

Experiment tracking and model registry

MLflow open-source platform for tracking experiments, packaging models, and managing the model lifecycle. The most widely adopted tool in this category.
Weights & Biases (W&B) experiment tracking with rich visualisation, hyperparameter sweep management, and team collaboration features. Popular in research and production.
Neptune.ai experiment tracking focused on enterprise needs with strong metadata management.

ML pipelines and orchestration

Kubeflow end-to-end ML platform built on Kubernetes. Handles training, pipelines, serving, and notebook management. The Kubernetes-native option.
Apache Airflow general-purpose workflow orchestration used by many teams for ML pipelines.
Prefect modern workflow orchestration with better developer experience than Airflow.

Model serving

vLLM purpose-built for large language model serving. Achieves 2-4x higher throughput than naive serving through continuous batching and PagedAttention.
Triton Inference Server NVIDIA's production server supporting multi-model, multi-framework serving with dynamic batching and GPU optimisation.
Seldon Core Kubernetes-native model serving with built-in A/B testing, canary deployments, and explainability.
BentoML framework for packaging and deploying ML models as production-ready API services.

Data versioning

DVC (Data Version Control) Git for data. Tracks large datasets alongside code without storing them in Git.
LakeFS Git-like version control for data lakes, enabling branching and merging of data.

Feature stores

Feast open-source feature store for managing and serving ML features consistently across training and serving.
Tecton managed feature platform with real-time feature computation.

Model monitoring

Evidently AI open-source tool for monitoring data drift, model performance, and data quality in production.
WhyLabs AI observability platform for monitoring model performance and data quality.
Arize ML observability with drift detection, performance monitoring, and root cause analysis.

Infrastructure

All the standard DevOps tools apply: Kubernetes for orchestration, Terraform for infrastructure provisioning, Prometheus and Grafana for metrics and dashboards, ArgoCD for GitOps deployment, and Docker for containerisation.

How DevOps skills transfer to MLOps

The transfer is remarkably direct. Here is the mapping:

DevOps Skill	MLOps Application
Docker	Containerising models and serving frameworks
Kubernetes	Orchestrating model serving, GPU scheduling, autoscaling
Terraform	Provisioning GPU instances, networking, storage, IAM
CI/CD (GitHub Actions, ArgoCD)	Automating model training, validation, and deployment pipelines
Prometheus + Grafana	Monitoring model latency, throughput, and GPU utilisation
Python scripting	Writing pipeline automation, data validation scripts, API endpoints
Networking	Configuring model serving endpoints, load balancers, ingress
Security	Managing model API authentication, secrets, data access controls
Cost optimisation	Managing GPU costs (spot instances, right-sizing, scheduling)

Roughly 70% of an MLOps engineer's daily work uses standard DevOps tools and practices. The remaining 30% involves ML-specific tooling experiment tracking, model registries, drift detection which sits on top of the DevOps foundation.

For a detailed side-by-side comparison, see our MLOps vs DevOps breakdown.

The MLOps Engineer career opportunity

The MLOps Engineer role is one of the fastest-growing positions in tech. Here is why:

Demand is outpacing supply

Every company deploying AI needs MLOps. But the supply of engineers with both infrastructure and ML knowledge is limited. Data scientists lack infrastructure skills. DevOps engineers lack ML context. The intersection is small, and companies are willing to pay a premium for it.

Job postings for MLOps Engineer roles have grown over 40% year-over-year since 2023, according to LinkedIn and Indeed data. This growth rate outpaces general DevOps roles (which are themselves growing at 25-30%).

The salary premium is real

MLOps roles command 10-25% more than equivalent DevOps positions:

Level	DevOps (UK)	MLOps (UK)	DevOps (US)	MLOps (US)
Mid-level	£55,000-£85,000	£65,000-£100,000	$100,000-$155,000	$120,000-$180,000
Senior	£80,000-£120,000	£90,000-£140,000	$140,000-$200,000	$160,000-$230,000
Lead/Staff	£100,000-£150,000	£120,000-£180,000	$170,000-$250,000	$200,000-$300,000

The premium exists because qualified candidates are scarce. AI companies the fastest-growing employers of infrastructure engineers pay at the top of these ranges.

DevOps engineers have the fastest path in

A DevOps engineer transitioning to MLOps needs approximately 6-8 weeks of additional learning:

Basic ML concepts (1-2 weeks) training, inference, evaluation metrics, overfitting, underfitting. You need conceptual understanding, not the ability to design novel architectures.
Model serving (1 week) deploying models with vLLM, Triton, or BentoML. Containerising model servers. Setting up inference endpoints.
GPU management (1 week) NVIDIA device plugins for Kubernetes, GPU scheduling, multi-GPU nodes, cost management for GPU instances.
ML pipeline tools (2 weeks) hands-on with MLflow for experiment tracking, Kubeflow or a similar tool for pipeline orchestration.
Monitoring and drift (1 week) setting up Evidently AI or similar for drift detection. Understanding data distribution monitoring.

Compare this to a data scientist learning DevOps from scratch Kubernetes, Terraform, CI/CD, cloud networking, monitoring, Linux which takes 4-6 months. The DevOps-to-MLOps path is dramatically shorter.

For a deeper look at why AI companies specifically seek DevOps talent, see why AI companies hire DevOps engineers. For a complete picture of AI infrastructure careers, read our guide to AI infrastructure.

The convergence of DevOps and MLOps

A clear trend is emerging: DevOps and MLOps are converging. As more companies deploy ML models, the expectation is shifting. Platform teams are expected to support both traditional applications and ML workloads on the same infrastructure.

Kubernetes is at the centre of this convergence. The same cluster that runs your web application can run your model serving infrastructure. The same Terraform that provisions your application databases can provision your GPU instances. The same CI/CD pipelines that deploy your API can deploy your model.

The engineers who understand both sides application infrastructure and ML infrastructure are the most versatile and highest-paid infrastructure professionals in the industry. Building a strong DevOps foundation and layering ML knowledge on top is the most efficient path to this position.

The rise of MLOps is not a separate movement from DevOps. It is DevOps expanding into the most important technology wave of the decade. The skills are the same. The tools are the same. The opportunities are bigger.