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Data pipelines are the backbone of Machine Learning projects. They are responsible for collecting, storing, and processing the data that is used to train and deploy machine learning models. Without a data pipeline, it would be very difficult to manage the large amounts of data that are required for machine learning projects.
Most companies today have multiple cloud instances with multiple cloud service providers (CSPs) as well as an on-premises environment. It’s complex, but that doesn’t make it inherently wrong—there are usually good business reasons behind the decisions. It does, however, create management challenges.