What is the primary purpose of running models on local machines during development?

The primary purpose of running models on local machines during development is to test without incurring additional costs. When developing data science models, it is common to use local environments because they do not require payment for cloud resources, making it cost-effective during the exploratory phases of development.

In this local setup, data scientists can iterate quickly on model designs, debug code, and explore datasets without the overhead associated with cloud resources. Since cloud platforms may charge based on computation time and data usage, performing this work locally helps manage budgets effectively, especially when experimenting and adjusting models frequently.

While there may be advantages to cloud computing resources, such as scalability and access to high-powered infrastructure, these benefits might not be as critical during the early stages of model development. Similarly, real-time collaboration is typically enhanced in cloud environments with tools designed for such purposes, but this is not the primary driver for working locally. While enhancing security is important, local development does not inherently provide better security than cloud environments; it often depends on the nature of the data and the security measures in place. Therefore, focusing on cost efficiency is a central reason for using local machines during the initial phases of model development.