End-to-end ML pipelines are an important term in the fields of artificial intelligence, automation and digital transformation. They refer to automated processes that cover all machine learning (ML) tasks from start to finish - without manual intervention at every step.
Imagine a company wants to develop an algorithm that automatically recognises spam emails. Several individual work steps are processed automatically in an end-to-end ML pipeline: From data collection to data preparation, training the model, checking the results and integrating the model into the company's own IT landscape. The pipeline ensures that each of these steps runs smoothly and automatically in sequence.
This allows companies to save a lot of time, resources and money, as developers no longer have to monitor each phase individually or start them manually. This makes end-to-end ML pipelines particularly attractive for decision-makers who want to make data-driven processes more efficient and simpler - without any complicated technology in the background.
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