An end-to-end data cleaning workflow is crucial for preparing tabular data for AI and ML projects, as emphasized by the principle "garbage in, garbage out." The text outlines various steps in the data cleaning process, using a modified Adult Census Income dataset to demonstrate common issues such as standardizing empty values, removing duplicate records, handling missing data, and addressing field and record level outliers. Techniques include using machine learning-based imputation methods like MissForest to fill in missing data and employing IsolationForest for detecting outliers. Redundant fields, such as those that are highly correlated or contain constant values, are removed to ensure a more efficient and accurate model training process. The article highlights the importance of these steps in enhancing the quality of synthetic data generated by Gretel’s models, ultimately leading to more successful AI/ML outcomes.