The text can be summarized as:
The article explains how to implement a custom training solution using unmanaged cloud service APIs, particularly focusing on using Google Cloud Platform (GCP). It addresses the limitations of managed training services and goes on to propose a straightforward solution for managing cloud-based ML training on GCP that offers more flexibility and control.
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How to Implement a Custom Training Solution Using Basic (Unmanaged) Cloud Service APIs
In previous posts (e.g., here) we have expanded on the benefits of developing AI models in the cloud. Machine Learning projects, especially large ones, typically require access to specialized machinery (e.g., training accelerators), the ability to scale at will, an appropriate infrastructure for maintaining large amounts of data, and tools for managing large-scale experimentation.
Motivation — Limitations of Managed Training Services
High-level solutions will typically prioritize ease-of-use and increased accessibility at the cost of reduced control over the underlying flow. Cloud-based managed training services are no different. Along with the convenience (as described above), comes a certain loss of control over the details of the training startup and execution.
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