El ciclo de vida del ML en una imagen

The whole ML lifecycle fits in one picture: data, train, evaluate, deploy, monitor, retrain. It is not a line, it is a circle that keeps turning. 🔄

Every model begins with data: collecting it, cleaning it, and labeling it. Garbage in here means garbage everywhere downstream.

Next you train: the model learns patterns from your prepared data. This is the step most people picture when they think of ML.

Before you trust a model you evaluate it on data it never saw. A held-out test set tells you if it really learned or just memorized.

To deploy means putting the model where it can answer real requests, usually behind an API so apps and users can call it.

Once live you monitor it: watch accuracy, latency, and incoming data so you notice trouble before your users do.

When the world drifts you retrain on fresh data, then loop back through evaluate and deploy. The cycle never truly ends.

Real outcomes flow back as new labeled data. This feedback loop is what turns a one-shot model into a system that improves over time.

Every stage has its own tooling: DVC for data, MLflow for training, FastAPI for serving. The lifecycle is your map to all of them.

Surprisingly, training is the small part. Teams spend most effort on data prep and the operations around the model, not the fitting itself.

The MLOps goal is to automate each handoff so the loop can turn with little manual effort, from new data all the way to a new live model.

Let us check that the lifecycle loop is clear in your mind.

Data, train, evaluate, deploy, monitor, retrain, then repeat. Hold this lifecycle in your head and every MLOps tool will find its place. 🗺️