Introduction to Model Development

AI modeling involves two stages:

• Development: Prepare and configure the environment, and debug code for training based on deep learning. ModelArts DevEnviron is recommended for code debugging.
• Experiment: Optimize the datasets and hyperparameters, and obtain an ideal model through multiple rounds of experiments. The ModelArts training platform is recommended for training.

In the two stages, code is designed, developed and tested in repeated cycles. In the development stage, when the code becomes stable, the modeling process enters the experiment stage, during which hyperparameters are continuously optimized to iterate the model. In the experiment stage, when the training performance can be optimized, the modeling process returns to the development stage for optimizing code.

Figure 1 Model development process

ModelArts provides model training, which allows you to view training results and tune model parameters based on the training results. You can select resource pools with different instance flavors for model training.

The following guides you to train models on ModelArts:

• Upload the labeled data to OBS. For details, see Preparing Data.
• Follow the instructions provided in Preparing Algorithms to use an algorithm for model training.
• Create a training job. You can perform this operation on the ModelArts console. For details, see Creating a Training Job. For details about how to create models using custom algorithms, see Using a Custom Algorithm to Build a Handwritten Digit Recognition Model.
• Follow the instructions provided in Training Job Logs to view training job logs and training resource usage.
• Follow the instructions provided in Stopping, Rebuilding, or Searching for a Training Job to stop or delete a training job.
• Follow the instructions provided in Automatic Model Tuning (AutoSearch) to automatically tune model hyperparameters.
• Troubleshoot if you encounter any problem during training. For details, see Troubleshooting.