PROTOPIA AI GLOSSARY

A

A method used to efficiently and accurately compute the gradient of a function with respect to its inputs.
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C

Computational cost refers to the computing resources required to complete a specific task. These resources can be memory, computation time, bandwidth, or
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Confidential Computing offers a hardware-based security solution designed to protect data during use with application-isolation technology.
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D

Data access is the ability for users to access their data given physical, software, or legal and policy-driven constraints.
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Data anonymization protects private or sensitive information by erasing or encrypting identifiers that connect an individual to the data.
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Data governance is the overall management of the availability, usability, integrity, and security of the data used in an organization
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Data labeling identifies objects on raw data such as images, text, videos, and audio. The goal is to provide one or more informative labels to provide context so that a machine learning model can learn from it.
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Data masking obscures or replaces sensitive information in a dataset to minimize exposure while maintaining the data’s functional value.
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Data ownership refers to the rights and responsibilities of individuals or organizations in the collection, storage, use, and distribution of data.
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Data perturbation changes an original dataset by applying techniques that round numbers and add random noise.
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Data control refers to the measures and processes put in place to manage the access, use, and dissemination of data within an organization.
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Data redaction refers to removing certain pieces of information from data, designed to keep that data from being linked to individuals or used for wrongdoing.
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Data sharing is making data available to other individuals or organizations. It involves data exchange between individuals, groups, or organizations,
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Data Tokenization is a process by which sensitive data is replaced by non-sensitive characters known as a token.
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Data-type agnostic is the property of a system, process, or algorithm that can handle and process different types of data without any bias towards any particular type of data.
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In data engineering and machine learning, a data pipeline refers to the steps involved in extracting, transforming, loading, and processing data.
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Deployment data refers to the data used to deploy and run a machine-learning model in a production environment.
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Differential privacy is a system for sharing information about a dataset by describing the patterns of groups within the dataset while withholding information about individuals
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A technique in machine learning and artificial intelligence that involves training models using gradient-based optimization.
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E

An edge system is a computing system that is located at the “edge” of a network, close to the source of the data.
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Encryption is the process of converting plaintext data into a coded form of data that can only be unlocked by someone who has the appropriate key.
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F

Federated Learning is a machine learning technique that enables training on a decentralized dataset distributed.
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G

Gradient-based optimization is a method used in machine learning and artificial intelligence to update the parameters of a model to minimize a loss function.
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Generative AI is a type of artificial intelligence that creates new data or content based on the patterns and structures it has learned from existing data.
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H

A form of encryption that allows computations to be performed directly on ciphertext without the need first to decrypt the data.
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I

Inference data is used to make predictions or inferences with a trained machine learning (ML) model.
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J

Jupyter Notebook is an open-source interactive computing platform that allows users to create and share documents that contain live code, equations, visualizations, and narrative text.
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L

Labeled data refers to data that has already been annotated or categorized with labels or tags that describe the content of the data.
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A language model (LLM) is a form of artificial intelligence that relies on massive amounts of textual data to understand and produce language
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M

A machine learning model is a mathematical representation of a system capable of learning from data and making predictions or decisions.
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Model drift, also known as concept drift, refers to a phenomenon in machine learning where the distribution of the data changes over time, and the trained model’s performance degrades.
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MLOps, an abbreviation for Machine Learning Operations, is a set of practices and processes for managing the end-to-end lifecycle of machine learning models.
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The machine learning (ML) life cycle refers to the stages in building, deploying, and maintaining a machine learning model.
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Model deployment refers to making a trained ML model accessible and usable in a real-world production environment by integrating it into a production system and monitoring its performance.
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N

Natural Language Processing (NLP) concerns interactions between computers and human (natural) languages. The goal is to enable computers to process, understand, and generate human language.
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Neural Network is a wide term in the field of AI that refers to any type of network that is trained to process data
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O

The optimization step is the iteration process of finding the best set of parameters or weights for a machine learning model to predict the outputs based on the inputs accurately.
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Open banking is a financial services model that allows third-party providers, such as fintech companies, to access bank customers’ financial data with their consent.
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P

Personally Identifiable Information, abbreviated as PII, refers to any information that can be used to identify a specific individual, such as name, address, driver’s license number, etc.
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PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
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R

Responsible AI is a set of practices and principles designed to ensure and promote the safe and ethical use of AI,
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S

Sensitive data is confidential, private, or protected by regulations. This may include personal information, financial information,
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Structured data refers to data that is organized in a tabular format with well-defined columns and rows.
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Synthetic data is artificially generated data that is used to mimic real-world data. Synthetic data is often used for testing and training machine learning models
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Semi-structured data does not follow any data model because it does not have a fixed schema. Unlike structured/tabular data, it lacks any rigid form
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T

Tabular data refers to data organized in a table format with rows and columns. Each row represents an instance or an observation, and each column represents a feature or an attribute of the cases.
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TensorFlow is an open-source software library for dataflow and differentiable programming across various tasks.
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Training data is the initial data feed into the system to train the ML algorithm. People (workforce), Process (business rules,
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The training model is a part of the data science lifecycle wherein datasets are used to train machine learning algorithms.
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A training model refers to a quantitative representation of a problem that is used to learn patterns and relationships in training data.
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Task-agnostic refers to algorithms or models that can be applied to various jobs, regardless of the specific task being performed.
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A trust boundary refers to a clear distinction between the parts of a system that are trusted to behave correctly and securely and those that are not.
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U

Unstructured data refers to data that cannot be easily processed and analyzed using traditional machine learning algorithms due to its lack of a predefined format.
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Data not tagged with labels identifying characteristics, properties, or classifications’. Unlabeled data has no labels or targets to predict, only features to represent them.
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