This is an iterative process of building Machine Learning solutions in a pipeline. Generally speaking, it consists of the following steps: data wrangling, model building and making predictions. The model is a simplified representation of a modeled process. The data phase includes data collection, data exploration, feature engineering, and splitting data into training and evaluation sets. After that, models are built, evaluated, and their performance is improved by hyperparameter tuning. With the model deployed, we can make predictions using new data. The last phase in the project lifecycle is model maintenance and support.