How to use Pickling to Save a Trained Machine Learning Model to use for Deployment Later
This video provides an overview of how to use pickle (a standard Python library) to save a trained machine learning model (or really, any Python object) as a binary file. This process is called pickling, or more generally, object serialization. The binary file can be unpickled, or more genearlly de-serialized, into a Python object again later, perhaps via a different Python process. The latter would be the case for deploying the machine learning model to a server to be used to make predictions for unseen instances coming in via client API requests. A simple list representation of a decision tree model is pickled, unpickled, and traversed via a recursive algorithm to make predictions for such requests.
The decision tree used is based on a toy "interview" dataset that I don't have a reference for (sorry! let me know in the comments if you know its original source). The tree was trained using an entropy-based TDIDT (top-down induction of decision trees) algorithm that is not covered in this video.
This video is part 4 of an 8 part series on APIs and machine learning model deployment with Python. The next video in this series is: • How to Deploy a Flask App to the Web ...
The previous video in this series is: • How to Create a Simple Flask Web App ...
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