Glossary

Term	Definition
AI	Artificial Intelligence (AI), machine or computer simulation of human cognitive processes, such as learning from data, recognizing patterns, and making decisions.
Basis	Basis data or basis features are those inputs to the ML model, which are provided by an external application in the prediction REST request.
Batch data	Data that is processed in large groups at scheduled times
Contextual	Contextual data or contextual features are those inputs to the ML model, which are fetched from data sources and were not part of the prediction REST request at the time of prediction.
CPU	A Central Processing Unit (CPU) is the primary processor in a computing system.
Data sink	A table in Aizen storage that corresponds to a data source. There are two types of data sinks: events and static.
Data source	The source or original location of the raw data that is used to train ML models. The data sources are external to the Aizen platform and are typically database tables (connected as JDBC endpoints), CSV files, or streaming sources (connected as Kafka endpoints).
DataOps	Data Operations (DataOps), a set of practices and technologies for improving data analytics
Dataset	A collection of related data that is used to train ML models
DL	Deep Learning (DL), a type of machine learning that uses artificial neural networks, similar to the human brain, to train computers to process data and make decisions based on examples
Entity	An object or concept that can be modeled and that has features associated with it. It is a key column in database terms. Examples are customer and product.
Feature	A feature is an individual measurable property. It is a column in database terms. Examples are user rating (product data) and humidity (weather data).
GPU	A Graphics Processing Unit (GPU) is an electrical circuit that can rapidly process large amounts of calculations simultaneously, thus making it useful for accelerating the training of ML models.
InfraOps	Infrastructure Operations (InfraOps), the management and maintenance of a company's IT infrastructure.
IoT	Internet of Things (IoT)
IPYNB	Interactive Python Notebook (IPYNB), a text-based file used by the Jupyter Notebook.
JSON	JavaScript Object Notation (JSON)
Label	The actual output value that an ML model is trying to learn.
LLM	Large Language Model (LLM) is a type of machine learning that processes and generates language.
ML	Machine Learning (ML), an area of artificial intelligence where computers use algorithms and statistical models to analyze input data and predict output data, steadily learning and improving performance over time
ML model	A machine learning (ML) model is an algorithm that has been trained on a dataset to identify patterns in the dataset and make predictions based on those patterns.
RAG	Retrieval-Augmented Generation (RAG)
SQL	Structured Query Language (SQL), a relational database programming language
Streaming data	Data that is processed continuously in real time as it arrives
MLOps	Machine Learning Operations (MLOps), a set of practices and technologies for managing the machine learning (ML) lifecycle
UDF	User-Defined Function (UDF)
YAML	Yet Another Markup Language (YAML)