MLOps and DevOps share tools and cultural heritage, but the underlying engineering problems are fundamentally different. Understanding the distinction matters for your career — it determines where your skills transfer, what you need to learn, and what the work actually feels like day to day.
The Core Difference: Deterministic vs Probabilistic Systems
DevOps: You're deploying deterministic software. Code either works or it doesn't. Unit tests give clear pass/fail signals. A deployment succeeds or fails. Monitoring tells you whether services are up. When something breaks, there's a definable root cause.
MLOps: You're deploying probabilistic systems. Models don't fail — they degrade. Accuracy drops by 2% over three weeks as input data distribution shifts. There's no clear pass/fail; there's a quality spectrum that requires constant measurement. What "working" means is defined by evaluation metrics, not binary health checks.
This single difference cascades through everything: how you monitor, how you test, what versioning means, what on-call looks like, and how you communicate with stakeholders about system state.
What DevOps Engineers Must Learn to Move Into MLOps
The core skill gaps
Experiment tracking
Why every training run needs to be logged: parameters, metrics, data version, code version. Tools: MLflow, Weights & Biases
Data versioning
Data is an artefact alongside code. A model trained on different data is a different model. Tools: DVC, Delta Lake
Model monitoring
Detecting gradual degradation rather than binary failure. Data drift, concept drift, output distribution monitoring. Tools: Evidently AI, Arize
Feature stores
Serving consistent features across training and inference — the training-serving skew problem is unique to ML systems
ML domain literacy
Enough understanding of training pipelines, evaluation metrics, and model lifecycle to communicate effectively with data scientists
What MLOps Has Borrowed from DevOps
The MLOps discipline has deliberately imported DevOps practices where they apply:
- CI/CD — extended to include model training triggers, evaluation gates before deployment, and automated rollback when model quality drops below threshold
- Infrastructure as code — Terraform and Helm for reproducible ML infrastructure provisioning
- Containerisation — Docker for reproducible training environments and consistent serving containers
- Observability — Prometheus, Grafana, and alerting extended to include model-specific metrics alongside infrastructure metrics
- Version control culture — extended to data (DVC) and models (MLflow model registry) alongside code (Git)
Career and Salary Comparison
Based on publicly advertised UK roles and LinkedIn Salary data, senior MLOps engineers typically earn £100,000–£150,000, somewhat above the £80,000–£120,000 range for senior DevOps engineers at comparable experience levels. The premium reflects the additional ML domain knowledge required and the relative scarcity of people with strong infrastructure skills and ML understanding.
Both roles have strong demand in the UK. MLOps roles are concentrated at companies with significant ML investment — AI-native companies, major banks, and large tech companies. DevOps/SRE roles exist across a much broader range of employers. If you want maximum job security and optionality, a DevOps background provides it; if you want to work specifically in the AI space, MLOps is a clearer path.
Where the Roles Merge: Platform Engineering
At the most senior levels, there's increasing convergence around "ML platform engineering" or "AI platform engineering" — a role that sits at the intersection of DevOps, MLOps, and data engineering. These roles are building the internal platforms that data scientists, ML engineers, and AI product teams all rely on. They command the highest salaries in the infrastructure space (typically £140,000–£200,000+ at UK companies with serious AI investment) and require the broadest skill set.
See the full MLOps Engineer career guide
Salary tables, required skills, UK companies hiring, and the full career path.
Frequently Asked Questions
Can a DevOps engineer become an MLOps engineer easily?
More easily than most transitions in tech. Your Kubernetes, CI/CD, and IaC skills transfer directly. The gaps to close: experiment tracking, data versioning, model monitoring, and ML domain literacy. Typically 3–6 months with focused effort.
Does MLOps pay more than DevOps?
Generally yes at comparable experience levels, based on publicly advertised UK roles. The premium reflects ML domain knowledge and relative scarcity of people with the combined skill set.
Do MLOps engineers write ML code?
Some do, some don't — depends on company size and structure. At smaller companies they often do; at larger companies with dedicated ML engineering teams, MLOps focuses on platform and infrastructure.
Is MLOps just DevOps with extra steps?
No. The fundamental difference is deploying probabilistic systems that degrade gradually vs deterministic software that works or fails. This changes monitoring, testing, versioning, and what on-call looks like.
What certifications cover both?
CKA for Kubernetes depth. Google Professional Machine Learning Engineer for MLOps-specific content. AWS and Azure have equivalent ML certifications with MLOps components.