Many machine learning (ML) models are Python pickle files under the hood, and it makes sense. The use of pickling conserves memory, enables start-and-stop model training, and makes trained models portable (and, thereby, shareable). Pickling is easy to implement, is built into Python without requiring additional dependencies, and supports serialization of custom objects. There's little doubt about why choosing pickling for persistence is a popular practice among Python programmers and ML practitioners. Pre-trained models are typically treated as "free" byproducts of ML since they allow the valuable intellectual property like algorithms and corpora that produced the model to remain private.

Many machine learning (ML) models are Python pickle files under the hood, and it makes sense. The use of pickling conserves memory, enables start-and-stop model training, and makes trained models portable (and, thereby, shareable). Pickling is easy to implement, is built into Python without requiring additional dependencies, and supports serialization of custom objects. There's little doubt about why choosing pickling for persistence is a popular practice among Python programmers and ML practitioners. Pre-trained models are typically treated as "free" byproducts of ML since they allow the valuable intellectual property like algorithms and corpora that produced the model to remain private.

Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. Thanks to technologies that generate, store and analyze huge sets of data, companies are able to perform tasks that previously were impossible. But the added benefit does come with its own setbacks, specifically from a security standpoint. With reams of data being generated and transferred over networks, cybersecurity experts will have a hard time monitoring everything that gets exchanged -- potential threats can easily go unnoticed.