Feature lifecycle management in a feature store is the process of managing the creation, modification, and retirement of features throughout their usage in a machine learning project.

To manage the lifecycle of features in Databricks, first, create a feature store table with the dataset. Then, use the feature store API to version the features, track their usage, and monitor their performance. Finally, retire the features that are no longer relevant or perform poorly, and create new versions of the features as needed.

Feature lifecycle management in a feature store is not necessary as it does not provide any significant benefits and can be avoided.

To manage the lifecycle of features in Databricks, manually track the features used at each step of the machine learning pipeline and decide when to retire or modify them based on their performance.