generic

Remember to assign the result of an operation to a variable or set the in-place parameter in the API.

Avoid unnecessary iterations. Use vectorized solutions instead of loops.

Check whether feature scaling is added before scaling-sensitive operations.

Hyperparameters should be set explicitly.

Free memory in time.

Set deterministic algorithm option to True during the development process, and use the option that provides better performance in the production.

Add a mask for possible invalid values. For example, developers should add a mask for the input for tf.log() API.

Set random seed explicitly during the development process whenever a possible random procedure is involved in the application.

Use Pipeline() API in Scikit-Learn or check data segregation carefully when using other libraries to prevent data leakage.

Use threshold-independent metrics instead of threshold-dependent ones in model evaluation.