Model training

Model retraining refers to the process of updating and improving a machine learning model by training it on new or additional data. Machine learning models, especially those based on deep learning algorithms, require large amounts of data to learn patterns and make accurate predictions.

However, over time, the data distribution in the real world can change, leading to a decrease in the model’s performance. Retraining the model with new data helps it adapt to these changes and maintain or improve its accuracy and reliability.

Using GPUs in the cloud offers cost-efficiency, scalability and flexibility. You access the latest and most powerful GPU technologies without the upfront costs of purchasing and maintaining hardware.

GPU acceleration is particularly beneficial for deep learning models, which often involve complex neural networks with millions of parameters.

For simpler machine learning tasks, such as linear regression or decision trees, GPU acceleration might not be necessary and can be performed efficiently on CPUs.

Yes, many cloud providers allow you to use multiple GPUs simultaneously, enabling even faster model training. This is especially useful for large-scale projects that require significant computational power.