What Is MLOps? A Practical Guide for Beginners¶
Introduction¶
A model is not production-ready just because it works in a notebook. MLOps is the engineering discipline that turns training, packaging, deployment, monitoring, and retraining into repeatable production workflows.
Why This Matters¶
Without MLOps, teams lose track of which data trained a model, which code produced it, which version is deployed, and whether predictions are still reliable.
Core Concepts¶
A production model needs versioned data, tracked experiments, packaged artifacts, deployment automation, monitoring, rollback options, and a retraining process.
Practical Example¶
A minimal production-minded workflow starts with commands that can run outside a notebook:
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python pipelines/train.py --data data/v1/train.csv --out models/churn.pkl
Expected output:
dataset=data/v1/train.csv
metric.f1=0.842
artifact=models/churn.pkl
How This Fits in a Production Workflow¶
The notebook can explain the idea, but production should run through scripts, CI jobs, containers, deployment manifests, and monitoring dashboards.
Common Mistakes¶
- Treating a notebook as the production pipeline.
- Deploying a model without knowing its training data version.
- Monitoring only CPU and memory while ignoring prediction quality.
- Replacing a production model without a rollback plan.
Quick Checklist¶
- Can the model be trained from a clean checkout?
- Is the dataset version recorded?
- Are metrics stored with the artifact?
- Is deployment automated and reversible?
- Are inference logs and drift signals monitored?
Related Guides¶
Summary¶
Learn what MLOps means from an engineering point of view and what is required to move models from notebooks to production.