Building Deep Learning Models on Databricks

Complete course on building, training, deploying, and managing the lifecycle of deep learning models on Databricks.

Training and evaluating models at scale starts with choosing the right compute environment. For moderate workloads or experimentation phases, a GPU on a single node is often sufficient and allows for rapid iteration. However, as data and model complexity grows, distributed clusters allow for efficient scaling and reduced training time.

These techniques ensure that, while increasing performance, model quality is maintained and overfitting is avoided.