Google Cloud Digital Leader Practice Exam

The primary purpose of using custom training in Google Cloud ML is to code your own machine learning (ML) environment and develop tailored models that fit specific use cases and data characteristics. Custom training provides the flexibility to build unique architectures, optimize algorithms, and implement specialized learning techniques that can leverage the particularities of the dataset being used.

When employing custom training, developers can write and modify their training code, specify data preprocessing steps, and define model behaviors according to their unique requirements and objectives. This approach is particularly beneficial for organizations seeking to improve model performance or to implement innovative features that might not be possible with pre-built models.

Pre-existing models generally offer quick solutions for common tasks but may not address specific needs effectively. Custom training allows for greater control and customization to achieve better results.