| 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) |
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