Four Challenges For Ml Data Pipeline

Data pipelines are essential for Machine Learning projects, as they are responsible for collecting, storing, and processing the data used to train and deploy models. The four major challenges for Data Engineers and Data Scientists are Volume, Velocity, Variety, and Veracity, which are collectively known as the 4V’s. Volume refers to the amount of data that needs to be processed, stored, and analyzed, and it affects the accuracy and performance of the model. Velocity refers to the need to process data in near-real time, as well as handle late-arrived data that may have errors. Variety refers to the different types of data sources, such as IoT sensors, servers, and video clips. Veracity refers to the need to ensure data quality and accuracy.