ML development at scale

Models may require GPU resources to train in reasonable times. GPUs use specialized hardware designed for parallel processing and simple mathematical operations, similar to the ones used in AI. As a result, they are preferred over CPUs for training large-dataset ML models due to their ability to handle multiple computations simultaneously. This significantly speeds up the training process. Our simple CIFAR-10 dataset, for instance, takes 90 minutes to train on a computer without a GPU and less than 5 minutes with an NVIDIA RTX 4090. GPUs are expensive and have high power requirements. An alternative is to use CPU and GPU computing on demand from cloud vendors.

Google Colab

Google Colab is a free, cloud-based service provided by Google that offers an interactive environment for ML development. It supports Python and provides a platform to create and execute Jupyter notebooks stored in Google Drive or imported from GitHub repositories.

Its focus is on...