Many teams still use DevOps vs MLOps interchangeably, even though these practices solve different engineering problems. DevOps focuses on software delivery and infrastructure, while MLOps adds everything required to manage machine learning models: data pipelines, experiments, versioning, and model behavior in production.
Because the boundaries are blurry for non-ML teams, it’s easy to misunderstand where DevOps ends and where MLOps becomes necessary. This article explains the difference between them in a simple, practical way — without academic theory.
What DevOps Actually Does
DevOps aims to make software development predictable. It automates CI/CD so teams can ship code without manual steps. It also covers Infrastructure as Code, cloud environments, logs, monitoring, security checks, and release stability. DevOps engineers work closely with developers to build clean pipelines, define environments, and eliminate deployment friction.
If a company needs reliable releases and scalable cloud systems, it usually relies on partners offering DevOps development services like AppRecode. Their focus is on building pipelines, containerized environments, and reproducible infrastructure — the foundations every modern product needs.
What MLOps Actually Does
MLOps extends DevOps into the ML lifecycle, where data and models behave unpredictably. In ML systems, developers track dataset versions, transform features, run experiments, train and validate models, and monitor them after deployment. Unlike code, a model can degrade simply because new data looks different.
MLOps handles everything DevOps doesn’t: feature stores, training pipelines, experiment tracking, model registries, drift detection, and automated retraining. It ensures that models remain accurate, not just deployed.
This entire process is usually implemented through specialized MLOps services such as AppRecode’s ML engineering practice, which builds pipelines for training, validation, deployment, and monitoring.
DevOps vs MLOps: Key Differences
The reason these differences exist is simple: traditional software behaves deterministically, while ML systems do not. Code either works or fails; a model can “work” yet gradually lose accuracy as real-world data shifts.
Engineers often describe the distinction in very practical terms — one of the most upvoted comments in a popular Reddit thread about DevOps vs MLOps explains it like this: DevOps maintains deployments, MLOps maintains behavior.
Where DevOps and MLOps Overlap (and Why You Often Need Both)
Both rely on automation, reproducible environments, and pipelines. MLOps doesn’t replace DevOps at all — it builds directly on top of it.
A typical progression looks like this:
- A company already has DevOps practices.
- ML functionality appears in the product.
- DevOps pipelines become insufficient for dataset and model workflows.
- MLOps is added to manage model evolution, drift, and retraining.
This transition is smoother when guided by a DevOps consulting company like AppRecode, which aligns DevOps foundations with ML-specific requirements.
Use Cases: When DevOps Is Enough & When You Need MLOps
DevOps is enough when:
- You’re building a SaaS app or internal system with no machine learning.
- Deployments are code-driven, not data-driven.
- You only need traditional CI/CD, monitoring, and infra automation.
MLOps becomes necessary when:
- You use recommendation systems, NLP models, forecasting, or scoring engines.
- Model accuracy changes over time due to new data (drift).
- Retraining needs to be automated, not manual.
- You have GPU-based or distributed training workloads.
If the product depends on predictions rather than static logic, MLOps is essential.
Why Work With AppRecode
AppRecode has separate DevOps and MLOps engineering groups — not a single blended team — which helps companies build both layers properly.
For infrastructure automation and cloud reliability, they provide DevOps support.
For model lifecycle automation, pipelines, and production ML deployments, they offer advanced MLOps services.
Their reputation is supported by transparent client feedback on Clutch, where AppRecode’s profile highlights engineering maturity, communication, and long-term reliability.
Conclusion
DevOps and MLOps solve different problems. DevOps stabilizes software delivery. MLOps stabilizes model behavior as data evolves.
If your product doesn’t rely on ML, DevOps alone is enough. If ML drives key functionality, MLOps becomes mandatory.
And for companies unsure where they stand, AppRecode can help evaluate the gap and build the right mix of DevOps and MLOps practices — so both code and models behave predictably at scale.
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