Glossary · 1 minute read
What Is MLOps?
MLOps (machine learning operations) is the set of practices for deploying, monitoring, and maintaining machine learning models reliably in production—bringing DevOps discipline to ML. It covers automated deployment, versioning of models and data, monitoring for drift and performance, retraining pipelines, and governance. MLOps matters because most models that work in a notebook fail in production without it: they can't be reliably deployed, break silently as data changes, and can't be traced or reproduced. MLOps is the capability that turns models into dependable systems.
MLOps is what keeps machine learning working after launch. Here's what it is, what it covers, and why models fail in production without it.
What MLOps is
MLOps (machine learning operations) is the set of practices for deploying, monitoring, and maintaining ML models reliably in production—bringing DevOps discipline to ML. See MLOps services.
What it covers
| Practice | Purpose |
|---|---|
| Automated deployment | CI/CD for models |
| Versioning | Models and data, reproducible |
| Monitoring | Catch drift and failures |
| Retraining | Keep models current |
| Governance | Auditable, controlled |
Why it matters
Most models that work in a notebook fail in production without MLOps—they can't be reliably deployed, break silently as data drifts, and can't be traced or reproduced. This is a top reason AI doesn't reach production.
MLOps is the missing capability
Many teams have data scientists but no MLOps engineers—which is exactly why their models don't ship or stay reliable. See data scientist vs ML engineer.
MLOps and reliability
MLOps is what turns a model into a dependable system—monitored, versioned, and maintained—underpinning model governance and incident response.
Why FISTA
FISTA Solutions brings MLOps discipline—deployment, monitoring, versioning, and retraining—so your models stay reliable in production, backed by a verified 99.9% uptime record across 150+ projects.
Getting models reliable in production? Talk to FISTA.
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01What is MLOps?
Machine learning operations—the practices for deploying, monitoring, and maintaining ML models reliably in production. It brings DevOps-style discipline to ML, covering deployment, versioning, monitoring, and retraining.
02Why is MLOps important?
Because most models that work in a notebook fail in production without it—they can't be reliably deployed, break silently as data drifts, and can't be traced or reproduced. MLOps is what keeps models dependable after launch.
03What does MLOps include?
Automated model deployment (CI/CD for ML), versioning of models and data, monitoring for drift and performance, retraining pipelines, and governance. Together these keep models reliable and auditable across their lifecycle.
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