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Machine Learning Pipelines
A Machine Learning Pipeline is a series of steps that an AI model goes through, from raw data to a deployed application. These steps typically include data acquisition, preprocessing, model training, evaluation, and deployment. Streamlining these stages makes the development process more efficient and reproducible.
In plain terms
Imagine a factory assembly line for building cars, where each station handles a specific task like body construction, engine installation, or painting.
Why it matters
They ensure consistency and automation in the AI development lifecycle, allowing for faster iteration and reliable model updates.
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