To evaluate execution efficiency, the same ANN-based churn prediction model was trained on two different environments using identical code, data, and hyperparameters.
- VS Code (Local Execution) 0.73 minutes
- Google Colab (Cloud Execution) 0.30 minutes
Google Colab completed training significantly faster due to optimized compute resources.
Local execution time depends heavily on system hardware and background processes.
Execution environment plays a crucial role in deep learning performance. Cloud-based platforms can offer faster experimentation cycles and improved productivity.
Cloud-based platforms such as Google Colab can substantially reduce model training time, enabling faster experimentation and iteration compared to local execution.
This experiment reinforces the importance of environment-aware benchmarking when designing and optimizing deep learning workflows for training and deployment.