Machine Learning

Machine learning process diagram

This diagram shows the general machine learning modeling process:

• Datasets: Import your data and save it in an accessible format for further processing.
• Quests: Prepare the data, apply an algorithm, train the model, and evaluate the model. Deploy the model to acquire predictions.
• Endpoints: View and test the deployed models.

Quests

A quest is the flow of activities that make up the AI model.

In the model there are several stages in the machine learning process:

• The quest starts from a dataset.
• Pre-processing steps are used to prepare the data by cleaning or transforming it.
• One or more algorithms are applied, and the model is trained.
• The outcomes are scored and evaluated.
• The best model is taken into production.

Endpoints

The endpoint is deployed from the real-time production quest. The endpoint represents the deployed model that is called to get real time predictions.

Easy ML

You can use the Easy ML feature to run experiments and create machine learning models.

In Easy ML you are guided through a multi-step process where you provide the dataset and general guidelines, assisted by automatic suggestions. All data science actions are performed automatically based on pre-defined logic integrated behind the scenes.

After the experiment is complete, you can generate a corresponding machine learning quest from the results for further manual tuning and model deployment.

Custom algorithms

You can package and deploy custom algorithm code in Infor AI.

After the custom algorithm is registered and deployed, you can use it in a quest to train a model and expose the model through an endpoint.

Algorithm Quick Reference

You can use the Algorithm Quick Reference to decide which algorithm, from the catalog of available algorithms, is most suitable to be applied to train the model, depending on the business case you are trying to solve, and the data that you have available.