A comprehensive guide to the ML life cycle, step by step with examples in Python
If you’ve been in the data science space for any amount of time, you’ve most likely heard this buzz term.
The machine learning life cycle.
It sounds fancy, but this is what it really boils down to:
- Machine learning is an active and dynamic process — it doesn’t have a strict beginning or end
- Once a model is trained and deployed, it will most likely need to be retrained as time goes on, thus restarting the cycle.
- There are steps within the cycle, however, that need to be followed in their proper order and executed carefully
When you Google the ML life cycle, each source will probably give you a slightly different number of steps and their names.
However, you will notice that for the most part, the cycle contains: problem definition, data collection and preprocessing, feature engineering, model selection and training, model evaluation, deployment, and monitoring.