The Machine Learning Lifecycle Explained¶

Introduction¶

The machine learning lifecycle starts before infrastructure. A team must define the problem, decide what prediction is useful, collect data, create features, train candidates, validate results, and decide whether the model is safe to deploy.

Why This Matters¶

MLOps fails when the ML lifecycle is unclear. If the target label, validation method, or success metric is wrong, no deployment platform can fix the model.

Core Concepts¶

The lifecycle covers problem definition, data collection, feature preparation, training, validation, test evaluation, and deployment readiness.

Practical Example¶

A practical lifecycle stores each step as a script:

python data/validate.py --input data/raw/customers.csv
python features/build.py --input data/raw/customers.csv --out data/features/train.parquet
python train.py --features data/features/train.parquet --model-out models/churn.pkl
python evaluate.py --model models/churn.pkl --test data/features/test.parquet

How This Fits in a Production Workflow¶

Before production, the lifecycle should produce validated data, feature definitions, trained model, evaluation report, test results, and a serving interface.

Common Mistakes¶

• Training on features that are unavailable during inference.
• Evaluating on data that leaked from training.
• Choosing a metric that does not match the business cost.
• Deploying without testing the prediction API.

Quick Checklist¶

• Is the target label defined?
• Are training and test splits documented?
• Are features reproducible?
• Is evaluation automated?
• Is inference behavior tested?

Related Guides¶

• What Is MLOps? A Practical Guide for Beginners
• Model Evaluation in MLOps
• How a Model Training Pipeline Works

Summary¶

Learn the machine learning lifecycle from problem definition through data, training, validation, testing, and deployment readiness.