Extending xpresso.ai Functionality

Developers can use the xpresso.ai APIs to extend xpresso.ai functionality. Some examples include:

• Scheduling a training pipeline

• Checking on the availability of new training data, and automatically starting a training pipeline on the new data

• Starting a training pipeline, monitoring its output metrics at regular intervals and pausing it if the metrics (e.g., training accuracy) are not satisfactory

• Starting a training pipeline, monitoring its output metrics at regular intervals, pausing it if the metrics (e.g., training accuracy) are not satisfactory and restarting it with changed hyperparameters

• Starting several training pipelines in parallel (with different algorithms / different hyperparameters), comparing them while running, and terminating unsatisfactory pipelines