Why might big data be detrimental to the machine learning process? (Select two.)

Big datasets can indeed pose challenges for machine learning algorithms, primarily due to their volume and complexity. The computational resources required to process large datasets can surpass the capabilities of standard algorithms, leading to inefficiencies or failures in successfully training models. For instance, algorithms may require extensive time for data loading and processing, which can slow down the overall workflow. There may also be limits in memory and processing power that can lead to bottlenecks, making it difficult to utilize machine learning effectively.

The second aspect that might be detrimental is related to computing performance. As datasets grow larger, the infrastructure needed to handle such data becomes more critical. Increased demand on CPU, GPU, and memory resources can result in higher latency and longer training times, which may ultimately affect the model's ability to learn effectively and generalize well.

While potential difficulties in obtaining large datasets or their impact on predictive performance might seem relevant, the primary focus here is on the challenges of processing them and the strain they can put on computing resources. Understanding these challenges is crucial for practitioners, as they need to ensure their systems are adequately equipped to handle big data effectively to optimize machine learning processes.