Large-scale machine learning is challenging for several reasons: Data volume: In large-scale machine learning, the volume of data can be massive, which makes it difficult to store, process, and analyze. Large datasets can require specialized hardware and software infrastructure to handle efficiently. Data variety: Large-scale machine learning often involves dealing with diverse and heterogeneous..

Pre-processing refers to the various data cleaning and transformation techniques applied to raw data before it is fed into a data pipeline. Pre-processing can improve the quality of data pipeline in several ways: Data quality: Pre-processing can help identify and eliminate or correct errors, inconsistencies, and missing values in the data, thus improving its quality and accuracy. This can help p..