MLOps
Operating machine learning systems reliably in production.
SpecializedadvancedGuide only -- no course yet
Overview
MLOps applies DevOps discipline (versioning, testing, monitoring, deployment automation) to machine learning systems specifically, addressing problems unique to ML -- model versioning, data drift, retraining pipelines -- that generic DevOps tooling doesn't fully cover.
- What it is
- The practices and tooling for deploying, monitoring, and maintaining machine learning models in production.
- Why it's used
- A trained model degrades over time as real-world data drifts from training data -- MLOps is the discipline for catching and responding to that.
- Where it fits
- After understanding machine learning and general DevOps/CI-CD practices; a specialization within ML engineering.
Core concepts
- Model versioning
- Data/model drift monitoring
- Automated retraining pipelines
- A/B testing models in production
Example
Unlike typical software, an ML model can 'break' silently -- it keeps returning predictions, just increasingly wrong ones -- which is why monitoring accuracy/drift, not just uptime, is central to MLOps.
// A minimal MLOps check, conceptually:
// if (currentModelAccuracy < deploymentThreshold) {
// alertTeam("Model accuracy has drifted below threshold");
// }Common use cases
- Operating production ML systems at scale
- Automated model retraining and deployment pipelines
Project ideas
- Design (on paper) a monitoring dashboard for a deployed model: what three metrics would you track, and why those three?