From the course: Artificial Intelligence Foundations: Machine Learning
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Structuring a machine learning pipeline
From the course: Artificial Intelligence Foundations: Machine Learning
Structuring a machine learning pipeline
- Have you ever pressed Shift+Enter or Run Cell inside of your Jupyter Notebook and thought to yourself, there has to be an easier and repeatable way to do this? Well, you would be right. And the answer you're looking for is called a pipeline. The data collection and preparation phase of machine learning consists of many steps, imputing missing data, handling outliers, understanding correlations, feature engineering, encoding, algorithm experimentation, and more. When training a model to predict the cost of homes, we did each step individually, and sometimes even repeated steps when experimenting with a different learning algorithm on the same dataset. Can you imagine supporting a production machine learning system this way? You'd want to use pipelines instead. Pipelines allow you to assemble all the steps of your machine learning workflow together. This assembly helps to streamline the workflow by grouping and automating…
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