The MLOps lifecycle is the operational, tooling-centric view of how a model moves from idea to continuously-maintained production system — distinct from the technical ML workflow (the modeling steps) and the business project lifecycle (scoping through monitoring at a high level), this note focuses specifically on the infrastructure stages — tracking, registries, CI/CD — that make ML operations repeatable at scale.
The Full Pipeline
The tooling-centric stages — Experiment Tracking, Model Registry, and CI/CD — are exactly what distinguishes an MLOps lifecycle from a plain modeling workflow.
Where This Differs From the ML Workflow and Project Lifecycle Notes
| Note | Focus |
|---|---|
| ML Workflow | The technical modeling steps a data scientist runs, once |
| ML Project Lifecycle | The business-facing stages (scoping, data, modeling, deployment, monitoring) at a high level |
| MLOps Lifecycle (this note) | The operational tooling that makes the whole thing repeatable, auditable, and automatically re-runnable |
The Three Stages That Make It "Ops," Not Just "ML"
- Experiment Tracking: every training run's parameters, metrics and artifacts are logged automatically — see Experiment Tracking
- Model Registry: a central, versioned catalog of trained models and their deployment status — see Model Registry
- CI/CD: automated testing and deployment of both code and models, not manual, ad hoc pushes — see ML CI/CD
Why the Loop Closes — "Continuous Training"
The pipeline doesn't end at deployment. Monitoring feeds directly back into retraining decisions, and a mature MLOps setup automates this entire loop — new data triggers validation, triggers retraining, triggers re-evaluation, and (if the new model passes quality gates) triggers redeployment, largely without manual intervention. This closed loop is what "continuous training" means, directly analogous to continuous integration/deployment in traditional software.
Practical Use Cases
- Designing the actual tooling and automation a team needs, beyond just "someone retrains it sometimes"
- Explaining to stakeholders why a model needs ongoing operational investment, not just an initial build
Common Mistakes
- Building the technical modeling pipeline well but skipping experiment tracking and a model registry entirely — this works fine until someone needs to debug or roll back a specific past model.
- Treating "monitoring" as the final stage instead of a loop that feeds back into retraining.
Interview Relevance
Q: "What makes an MLOps lifecycle different from a standard ML modeling workflow?" The addition of operational, automation-focused stages — experiment tracking, a model registry, CI/CD, and a closed retraining loop — that make the process repeatable, auditable and largely automatic, rather than a one-time sequence of steps a data scientist runs manually.
Practice Question
Explain, in your own words, why "Continuous Training" loops back to "Data Collection" rather than the pipeline simply ending at "Deployment."
