What are the common pitfalls to avoid when scaling machine learning models on cloud GPUs?

Scaling machine learning models on cloud GPUs offers powerful hardware access but requires careful management to avoid common mistakes that can increase costs or slow progress. Key pitfalls include using overly powerful GPUs, neglecting cost-effective instance options, and allowing GPUs to sit idle. It's crucial to match GPU resources to workload requirements, leverage spot and community instances to save on expenses, and implement strategies to maximize GPU utilization. Proper data management is essential to prevent bottlenecks, and environment setup needs careful attention to avoid runtime errors. Continuous cost monitoring and strategic scaling are vital to ensure efficient cloud GPU use, with platforms like Runpod offering features to help manage these aspects, including on-demand GPU selection, spot pricing, and automation to prevent unnecessary spending and resource waste.