Artificial Intelligence

Infor AI

Infor’s machine learning platform, a component of Infor OS.

Quest

A flow of activities that make up the machine learning model.

Training Quest

A quest involving a predictive method that produces a trained model.

Trained Model

A model that can be used to predict outcomes based on new data.

Production Quest

A trained model that has taken steps to deployment.

Endpoint

The REST API access point of a real time production quest to process new data through the model. Endpoints can process data by being passed a CSV, a JSON message, or accessed via the API gateway.

Data Lake

Flexible and economical cloud object storage solution where data is stored in its raw format. This is Infor AI’s primary data source.

Label/Target

Terms that refer to the prediction of the model.

Categorical

Data types that are non-numeric in nature and belong to a category instead. E.g. “Country of Residence”.

Supervised Learning

Machine learning algorithms that form relationships between targeted label and input features so that the output values for unseen data can be predicted. Supervised algorithms must be trained on known outcomes.

Unsupervised Learning

Machine learning algorithms that make inferences from data using only input features without referring to known or labelled outcomes. These algorithms can discover data structures by clustering it into intuitive groups.

Select Columns

Select or exclude a subset of columns from the current dataset.

Remove Duplicates

Remove duplicates in selected features.

Construct Feature

Create a new feature out of the existing ones by using mathematical, logical, or casting operations.

Index Data

Transform categorical values into numeric for the selected columns. Each category will be assigned a number according to its occurrence in the data, highest occurrence having number 0.

Smooth Data

Remove noise from a dataset to allow natural patterns to stand out.

Split Data

Split the dataset into training data and test data by specifying the split ratio for the training dataset.

Scripting

Execute a customized Python script to perform an activity which is not available in the catalog.

Ingest to Data Lake

Ingest data to Infor Data Lake

One Hot Encoder

Transform categorical features into a binary matrix (vectors) to distinguish each categorical label. The vector consists of 0s in all cells, with the exception of a single 1 in a cell used uniquely to identify the label.

Feature Scaling

Scale features with varying magnitudes, units and range into normalized values.

Handle Missing Data

Replace missing values in selected features (with mean / mode / constant value / interpolation), or remove the entire row exceeding a selected ratio of missing data.

Target Encoder

Numerization of categorical variables via target – replaces the categorical variable with just one new numerical variable and replaces each category of the categorical variable with its corresponding probability of the target (if categorical) or average of the target (if numerical)

Edit Metadata

Select the Target label. Edit the metadata of the selected features by changing its data type, tagging the categorical values, changing the variable name or defining their machine learning type.

Balance Data

Balance the dataset using undersampling or oversampling methods.

Execute SQL

SQL operations (filter out data, join datasets, aggregate data etc.).