Machine learning
pipeline and Mlops
Machine Learning model development is an iterative process; good model building depends on suitable preceding tasks which include: data cleansing, data balancing, data normalization, feature engineering, and hyper-parameters selection. The quality of models depends upon the hyper-parameter optimization and model performance (based on selected metrics) on validation and test sets and the generalization methods/techniques used. To improve model generalization performance, models need to be continuously trained on new data due to concept drift in the data.
