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Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
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Features don’t appear out of thin air – they are usually constructed using various transformations on raw data, that was either part of a data-set or received at runtime from a user.
We refer to that raw data as the raw_inputs of a model.
For example, your dataset might contain a column with state names – you will then have some preprocessing code convert that raw_input to a feature using one-hot encoding.