Infor Artificial Intelligence User Guide (Cloud)

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About this guide
Overview
Terminology
Components
Data collection
Datasets
Groups
Machine Learning
Machine learning process diagram
Quests
Endpoints
Easy ML
Custom algorithms
Algorithm Quick Reference
Optimization
Optimization platform process diagram
Quests
Endpoints
Custom algorithm
Solver Quick Reference
Decision Manager
Configuration Management tags
About Artificial Intelligence
Security roles
Navigation
Starting the application
Icons
Using the Quest modeler
Overview pages
Filters
Sorting
Viewing version details
Data collection
Datasets
Importing data from a local system
Importing data from Infor Data Lake
Saving a dataset from a machine-learning quest
Saving a static dataset
Saving a dynamic dataset
Exporting a dataset as a .csv file
Dataset status
Groups
Creating the group
Group status transitions
Machine learning
Machine learning quests
Quest Modeler
Training quest
Building a training quest
Activity catalog
Importing data
Preparing the data
Prepare the Data Activity catalog
Applying an algorithm
Apply algorithm activity catalog
XG Boost
Linear Learner
Random Forest
Decision Tree
Extra Trees
Multilayer Perceptron
DeepAR
K-Means
Auto Clustering
Principal Component Analysis
Custom Algorithm
Training the model
Saving the trained model
Models Library
Training the model incrementally
Model interpretability for models trained with a custom algorithm
Evaluating the model
Exploring the data
Quest validation
Running a training quest
Running a training quest without computing statistics
Column availability in post-processing
Training quest status
Selecting activities for the production quest
Machine learning production quest
Batch production
Realtime production
Apply Rules activity in Realtime production quest
Using the Apply Rules activity
Deploying a machine learning endpoint
Machine learning endpoints
Testing the machine learning endpoint
Testing the machine learning endpoint from a file
Testing the machine learning endpoint from a JSON message
Machine learning endpoint history logs
Machine learning endpoint status
Using the machine learning endpoint as an adopted application
Model maintenance
Updating the model manually
Updating the model on a schedule
Creating a workflow
Setting up a workflow schedule
Post conditions
Easy ML
Creating an Easy ML experiment
Easy ML experiment status
Machine learning custom algorithms
Python version
Uploading a source file
Defining your own requirements
Jupyter notebook
Using the Jupyter notebook
Custom algorithm examples
Custom algorithm status
Content deployment and configuration
Adding the dataset
Creating the quest and deploying the endpoint
Optimization
Optimization quests
Quest detail
Design quest
Building a design quest
Activity catalog
Dataset collection
Prepare the data
Prepare the Data activity catalog
Setting up the model
For equation building
For custom algorithms
Setup model configuration
Sets/Indices & Constant tab
Decision variables tab
Constraints tab
Objective functions tab
Using the Expression builder
Optimizing the model
Optimization results
Quest validation
Running a design quest
Design quest status
Moving the model to a production quest
Optimization production quest
Batch production
Realtime production
Deploying an optimization endpoint
Optimization endpoints
Testing the optimization endpoint
Testing the optimization endpoint from a file
Testing the optimization endpoint from a JSON message
Optimization endpoint history logs
Optimization endpoint status
Using the optimization endpoint as an adopted application
Model maintenance
Updating the model manually
Updating the model on a schedule
Creating a workflow
Setting up a workflow schedule
Post conditions
Optimization custom algorithms
Uploading a source file
Custom algorithm examples
Custom algorithm status
AI Advisor widget
Configuring the widget
Creating a workspace and adding the widget in Infor OS Portal
Configuring the widget manually
Publishing the widget
Locating the widget ID
Configuring the in-context application in Infor Ming.le
Decision Manager
Components
Decision plans
Creating a plan
Duplicating a plan
Importing a plan
Configuring the data dictionary
File input profile configuration
Manual profile and field configuration
Transforming data
Configuring transforms
Adding transformations
Rule decisions
Creating a rule decision
Defining activation
Defining decision attributes
Defining arbitration
Configuring rule facts
Rule editor
Like operator pattern matching
Numeric operators
String operators
String lists
Boolean
Boolean lists
Interaction routes
Available handlers
Adding the plan object schema to the data catalog
Testing the plan configuration
Activating the plan
Best practices
Machine learning best practices
Components overview
Business case definition
Fetching data
Data preparation
Model training and testing
Model fitting and tuning
Model deployment
Model maintenance
Optimization best practices
Components overview
Business case definition
Fetching data
Data preparation
Design and test the model
Design and tune the model
Model deployment
Batch production
Realtime production
Model maintenance
Troubleshooting
Machine learning
Datasets
Quests
Quests: Long execution times
Endpoints
Optimization
Datasets - Troubleshooting
Groups
Quests
Endpoints
Python libraries
Machine learning scripting libraries
Scripting activity - EMR
Scripting activity - lightweight
Machine learning custom algorithm libraries
Custom algorithms - Python 3.7
Custom algorithms - Python 3.12
Custom algorithm safe list
Optimization custom algorithm libraries
Python library updates
Machine learning scripting libraries
Scripting activities - EMR
Scripting activity - lightweight
Machine learning custom algorithm libraries
Custom algorithms - Python 3.7
Custom algorithms - Python 3.12
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