To build custom models supported by AI Center, you can use a Python IDE or an AutoML platform of

your choice. A Python IDE is a software application that provides tools and features for writing,

editing, debugging, and running Python code. An AutoML platform is a service that automates the

process of building and deploying machine learning models, such as data preprocessing, feature

engineering, model selection, hyperparameter tuning, and model evaluation. Some examples of

Python IDEs are PyCharm, Visual Studio Code, and Jupyter Notebook. Some examples of AutoML

platforms are Google Cloud AutoML, Microsoft Azure Machine Learning, and DataRobot.

To use a Python IDE, you need to install the required Python packages and dependencies, write the

code for your model, and test it locally. Then, you need to package your model as a zip file that

follows the AI Center ML Package structure and requirements. You can then upload the zip file to AI

Center and create an ML Skill to deploy and consume your model.

To use an AutoML platform, you need to sign up for the service, upload your data, configure your

model settings, and train your model. Then, you need to export your model as a zip file that follows

the AI Center ML Package structure and requirements. You can then upload the zip file to AI Center

and create an ML Skill to deploy and consume your model.

Reference:🔍AI Center - Building ML Packages,🔍AI Center - ML Package Structure,🔍AI Center - Creating

ML Skills