Data scientists and ML engineers evaluating whether to use AutoML platforms (H2O AutoML, Google Vertex AutoML, Auto-sklearn, FLAML, or Ludwig) versus custom modelling for a new project need structured guidance on when AutoML adds value, which platform fits the task, and how to integrate AutoML outputs into a production pipeline.