Which feature is essential for the integration of machine learning workflows in HPE GreenLake?

[Reference: HPE GreenLake for ML, Efficient Handling of Large Datasets:, Machine learning workflows often involve large datasets that require substantial processing power. GPUs are well-suited for handling these large datasets efficiently, enabling faster data processing and model training., Reference: HPE GreenLake GPU Acceleration, Enhanced ML Frameworks:, Many popular ML frameworks, such as TensorFlow and PyTorch, are optimized to leverage GPU acceleration. This optimization ensures that ML workflows can take full advantage of the available hardware resources., Reference: HPE GreenLake ML Frameworks, Scalability:, HPE GreenLake’s infrastructure allows for scalable GPU resources, which can be adjusted based on the workload requirements. This scalability ensures that businesses can efficiently manage their ML projects., Reference: HPE GreenLake Scalability, In summary, GPU acceleration is a critical feature for integrating machine learning workflows in HPE GreenLake, providing the necessary computational power and efficiency for ML tasks., References:, HPE GreenLake for ML, HPE GreenLake GPU Acceleration, HPE GreenLake ML Frameworks, HPE GreenLake Scalability, , ]