AI Governance Glossary
AI Governance Glossary
AI Governance Glossary
This glossary provides definitions and explanations of key terms and concepts related to the management, oversight, and ethical use of artificial intelligence (AI) systems.
AI Audit
Introduces a meticulously orchestrated audit program aimed at educating senior executives, unveiling inherent risks, and establishing safeguard controls, all in the pursuit of sound AI governance.
Introduces a meticulously orchestrated audit program aimed at educating senior executives, unveiling inherent risks, and establishing safeguard controls, all in the pursuit of sound AI governance.
Introduces a meticulously orchestrated audit program aimed at educating senior executives, unveiling inherent risks, and establishing safeguard controls, all in the pursuit of sound AI governance.
AI Explainability
AI explainability stands as a cornerstone in bridging the gap between the complex outputs of machine learning algorithms and human comprehension.
AI explainability stands as a cornerstone in bridging the gap between the complex outputs of machine learning algorithms and human comprehension.
AI explainability stands as a cornerstone in bridging the gap between the complex outputs of machine learning algorithms and human comprehension.
AI Observability
Stands as a comprehensive and sophisticated approach aimed at unveiling the intricate workings of machine learning models throughout their lifecycle.
Stands as a comprehensive and sophisticated approach aimed at unveiling the intricate workings of machine learning models throughout their lifecycle.
Stands as a comprehensive and sophisticated approach aimed at unveiling the intricate workings of machine learning models throughout their lifecycle.
AI Black Box
A notion prevalent in the world of machine learning, encapsulates the utilization of models that resist comprehension solely through scrutinizing their parameters.
A notion prevalent in the world of machine learning, encapsulates the utilization of models that resist comprehension solely through scrutinizing their parameters.
A notion prevalent in the world of machine learning, encapsulates the utilization of models that resist comprehension solely through scrutinizing their parameters.
Bias in ML
In the realm of machine learning, the specter of bias looms as a critical challenge, demanding vigilance and ethical considerations. Let's delve into the intricacies of this phenomenon.
In the realm of machine learning, the specter of bias looms as a critical challenge, demanding vigilance and ethical considerations. Let's delve into the intricacies of this phenomenon.
In the realm of machine learning, the specter of bias looms as a critical challenge, demanding vigilance and ethical considerations. Let's delve into the intricacies of this phenomenon.
Ethical AI
Ethical AI establishes a comprehensive framework encompassing guidelines concerning individual rights, privacy, behavior manipulation, and non-discrimination.
Ethical AI establishes a comprehensive framework encompassing guidelines concerning individual rights, privacy, behavior manipulation, and non-discrimination.
Ethical AI establishes a comprehensive framework encompassing guidelines concerning individual rights, privacy, behavior manipulation, and non-discrimination.
Concept Drift
Concept drift arises when real-world changes lead to shifts in variables, rendering previously learned patterns obsolete. This phenomenon detrimentally impacts the predictive capability of machine learning models, as their acquired knowledge becomes invalid.
Concept drift arises when real-world changes lead to shifts in variables, rendering previously learned patterns obsolete. This phenomenon detrimentally impacts the predictive capability of machine learning models, as their acquired knowledge becomes invalid.
Concept drift arises when real-world changes lead to shifts in variables, rendering previously learned patterns obsolete. This phenomenon detrimentally impacts the predictive capability of machine learning models, as their acquired knowledge becomes invalid.
CI/CD for ML
Enabling the seamless orchestration of source code development, automated testing, and continuous delivery, CI/CD for ML pipelines stands as a foundational practice within modern software engineering.
Enabling the seamless orchestration of source code development, automated testing, and continuous delivery, CI/CD for ML pipelines stands as a foundational practice within modern software engineering.
Enabling the seamless orchestration of source code development, automated testing, and continuous delivery, CI/CD for ML pipelines stands as a foundational practice within modern software engineering.
Data & Model Versioning
In the intricate landscape of machine learning (ML) development, the concept of data and model versioning emerges as a cornerstone. This practice, akin to the meticulous archiving of progress, involves uniquely naming multiple iterations of ML models deployed at distinct stages.
In the intricate landscape of machine learning (ML) development, the concept of data and model versioning emerges as a cornerstone. This practice, akin to the meticulous archiving of progress, involves uniquely naming multiple iterations of ML models deployed at distinct stages.
In the intricate landscape of machine learning (ML) development, the concept of data and model versioning emerges as a cornerstone. This practice, akin to the meticulous archiving of progress, involves uniquely naming multiple iterations of ML models deployed at distinct stages.
Data Drift
Data drift, a phenomenon of paramount significance, emerges when the distribution of features within training and serving data shifts, even as the fundamental relationship between input and target variables remains unchanged.
Data drift, a phenomenon of paramount significance, emerges when the distribution of features within training and serving data shifts, even as the fundamental relationship between input and target variables remains unchanged.
Data drift, a phenomenon of paramount significance, emerges when the distribution of features within training and serving data shifts, even as the fundamental relationship between input and target variables remains unchanged.
Training-Serving Screw
Training-serving skew refers to the discrepancy between a machine learning model's performance during training and its performance when deployed for real-world predictions, often arising from data handling differences and distribution shifts.
Training-serving skew refers to the discrepancy between a machine learning model's performance during training and its performance when deployed for real-world predictions, often arising from data handling differences and distribution shifts.
Training-serving skew refers to the discrepancy between a machine learning model's performance during training and its performance when deployed for real-world predictions, often arising from data handling differences and distribution shifts.
Data Segments
Data segments facilitate efficient data analysis by partitioning and clustering similar data based on the chosen parameter or filter, enhancing the analytical process.
Data segments facilitate efficient data analysis by partitioning and clustering similar data based on the chosen parameter or filter, enhancing the analytical process.
Data segments facilitate efficient data analysis by partitioning and clustering similar data based on the chosen parameter or filter, enhancing the analytical process.
Model Drift
Model drift, a nuanced facet in the realm of machine learning, pertains to the erosion of an ML model's predictive efficacy due to transformations in the digital landscape and consequential shifts in variables, including data and concepts.
Model drift, a nuanced facet in the realm of machine learning, pertains to the erosion of an ML model's predictive efficacy due to transformations in the digital landscape and consequential shifts in variables, including data and concepts.
Model drift, a nuanced facet in the realm of machine learning, pertains to the erosion of an ML model's predictive efficacy due to transformations in the digital landscape and consequential shifts in variables, including data and concepts.
Pipelines & Processes Automation
Automated Machine Learning (AutoML) serves as a conduit for streamlining the entire machine learning pipeline, encompassing data extraction, processing, and storage. This orchestrated automation brings forth scalability, effectiveness, and increased productivity to the forefront.
Automated Machine Learning (AutoML) serves as a conduit for streamlining the entire machine learning pipeline, encompassing data extraction, processing, and storage. This orchestrated automation brings forth scalability, effectiveness, and increased productivity to the forefront.
Automated Machine Learning (AutoML) serves as a conduit for streamlining the entire machine learning pipeline, encompassing data extraction, processing, and storage. This orchestrated automation brings forth scalability, effectiveness, and increased productivity to the forefront.
ML Monitors
Machine Learning (ML) monitors serve as indispensable tools that play a vital role in scrutinizing model performance, pinpointing issues in prediction quality, and swiftly alerting stakeholders to real-time developments.
Machine Learning (ML) monitors serve as indispensable tools that play a vital role in scrutinizing model performance, pinpointing issues in prediction quality, and swiftly alerting stakeholders to real-time developments.
Machine Learning (ML) monitors serve as indispensable tools that play a vital role in scrutinizing model performance, pinpointing issues in prediction quality, and swiftly alerting stakeholders to real-time developments.
Model Behavior
Model behavior encompasses both global and local interpretations, encompassing features, target predictions, and insights derived from data segments.
Model behavior encompasses both global and local interpretations, encompassing features, target predictions, and insights derived from data segments.
Model behavior encompasses both global and local interpretations, encompassing features, target predictions, and insights derived from data segments.
Model Fairness
Automated Machine Learning (AutoML) serves as a conduit for streamlining the entire machine learning pipeline, encompassing data extraction, processing, and storage. This orchestrated automation brings forth scalability, effectiveness, and increased productivity to the forefront.
Automated Machine Learning (AutoML) serves as a conduit for streamlining the entire machine learning pipeline, encompassing data extraction, processing, and storage. This orchestrated automation brings forth scalability, effectiveness, and increased productivity to the forefront.
Automated Machine Learning (AutoML) serves as a conduit for streamlining the entire machine learning pipeline, encompassing data extraction, processing, and storage. This orchestrated automation brings forth scalability, effectiveness, and increased productivity to the forefront.
Prediction
Predictions, the outcome of a machine learning (ML) algorithm fed with historical input data, play a pivotal role in generating actionable insights and informed decision-making.
Predictions, the outcome of a machine learning (ML) algorithm fed with historical input data, play a pivotal role in generating actionable insights and informed decision-making.
Predictions, the outcome of a machine learning (ML) algorithm fed with historical input data, play a pivotal role in generating actionable insights and informed decision-making.
Feature Management
A feature store serves as a centralized repository within an organization for curated features.
A feature store serves as a centralized repository within an organization for curated features.
A feature store serves as a centralized repository within an organization for curated features.
LLMOps
LLMOps represents a progressive facet within the MLOps domain, singularly dedicated to the operationalization of expansive language models (LLMs).
LLMOps represents a progressive facet within the MLOps domain, singularly dedicated to the operationalization of expansive language models (LLMs).
LLMOps represents a progressive facet within the MLOps domain, singularly dedicated to the operationalization of expansive language models (LLMs).
ML Diagnostics
In the realm of machine learning, the concept of diagnostics takes on a pivotal role in identifying challenges and refining models at various junctures of development.
In the realm of machine learning, the concept of diagnostics takes on a pivotal role in identifying challenges and refining models at various junctures of development.
In the realm of machine learning, the concept of diagnostics takes on a pivotal role in identifying challenges and refining models at various junctures of development.
ML Governance
ML Governance serves as a foundational internal framework within enterprises, orchestrating the control, execution, and oversight of ML development endeavours while strategically assigning roles and responsibilities.
ML Governance serves as a foundational internal framework within enterprises, orchestrating the control, execution, and oversight of ML development endeavours while strategically assigning roles and responsibilities.
ML Governance serves as a foundational internal framework within enterprises, orchestrating the control, execution, and oversight of ML development endeavours while strategically assigning roles and responsibilities.
ML Model Metrics
ML model metrics serve as the yardstick for assessing the effectiveness of machine learning models. These metrics play a pivotal role in the machine learning pipeline, offering validation and insights into model performance.
ML model metrics serve as the yardstick for assessing the effectiveness of machine learning models. These metrics play a pivotal role in the machine learning pipeline, offering validation and insights into model performance.
ML model metrics serve as the yardstick for assessing the effectiveness of machine learning models. These metrics play a pivotal role in the machine learning pipeline, offering validation and insights into model performance.
ML Reproducibility
ML reproducibility stands as a pivotal facet in the landscape of machine learning, ensuring that the processes, insights, and outcomes generated in the past can be accurately replicated in the present
ML reproducibility stands as a pivotal facet in the landscape of machine learning, ensuring that the processes, insights, and outcomes generated in the past can be accurately replicated in the present
ML reproducibility stands as a pivotal facet in the landscape of machine learning, ensuring that the processes, insights, and outcomes generated in the past can be accurately replicated in the present
MLOps Tools
MLOps tools are the linchpin of modern machine learning endeavors, simplifying intricate development processes and ushering in a new era of maintainability and auditability for ML experiments.
MLOps tools are the linchpin of modern machine learning endeavors, simplifying intricate development processes and ushering in a new era of maintainability and auditability for ML experiments.
MLOps tools are the linchpin of modern machine learning endeavors, simplifying intricate development processes and ushering in a new era of maintainability and auditability for ML experiments.
ModelOps
ModelOps represents a comprehensive strategy geared towards facilitating the seamless operationalization of machine learning (ML) models, thereby delivering anticipated business value to enterprises.
ModelOps represents a comprehensive strategy geared towards facilitating the seamless operationalization of machine learning (ML) models, thereby delivering anticipated business value to enterprises.
ModelOps represents a comprehensive strategy geared towards facilitating the seamless operationalization of machine learning (ML) models, thereby delivering anticipated business value to enterprises.
ML Scalability
Machine learning (ML) scalability entails empowering ML models to seamlessly handle vast datasets and execute numerous computations in a manner that is both economical and time-efficient..
Machine learning (ML) scalability entails empowering ML models to seamlessly handle vast datasets and execute numerous computations in a manner that is both economical and time-efficient..
Machine learning (ML) scalability entails empowering ML models to seamlessly handle vast datasets and execute numerous computations in a manner that is both economical and time-efficient..
ML Stack
The ML stack serves as a comprehensive reference model, outlining the essential infrastructure components crucial for constructing, training, deploying, and scaling machine learning systems.
The ML stack serves as a comprehensive reference model, outlining the essential infrastructure components crucial for constructing, training, deploying, and scaling machine learning systems.
The ML stack serves as a comprehensive reference model, outlining the essential infrastructure components crucial for constructing, training, deploying, and scaling machine learning systems.
Managing AI Artifacts
AI Artifacts constitute the invaluable outcomes derived from the training process, bearing significance throughout the various phases of the machine learning (ML) lifecycle..
AI Artifacts constitute the invaluable outcomes derived from the training process, bearing significance throughout the various phases of the machine learning (ML) lifecycle..
AI Artifacts constitute the invaluable outcomes derived from the training process, bearing significance throughout the various phases of the machine learning (ML) lifecycle..
Ground Truth
In the realm of machine learning (ML), the concept of ground truth holds paramount importance. It refers to factual data that is either observed or computed and can be subjected to objective analysis within a specific ML use case.
In the realm of machine learning (ML), the concept of ground truth holds paramount importance. It refers to factual data that is either observed or computed and can be subjected to objective analysis within a specific ML use case.
In the realm of machine learning (ML), the concept of ground truth holds paramount importance. It refers to factual data that is either observed or computed and can be subjected to objective analysis within a specific ML use case.
ML Lifecycle
The machine learning lifecycle embodies a sequential series of steps integral to data science projects. These steps form a cyclic process, each carrying significant weight and executed in a meticulously defined order.
The machine learning lifecycle embodies a sequential series of steps integral to data science projects. These steps form a cyclic process, each carrying significant weight and executed in a meticulously defined order.
The machine learning lifecycle embodies a sequential series of steps integral to data science projects. These steps form a cyclic process, each carrying significant weight and executed in a meticulously defined order.
ML Models
Machine Learning (ML) models are intricate constructs designed to decipher patterns, infer insights, and make predictions from data.
Machine Learning (ML) models are intricate constructs designed to decipher patterns, infer insights, and make predictions from data.
Machine Learning
The machine learning lifecycle embodies a sequential series of steps integral to data science projects. These steps form a cyclic process, each carrying significant weight and executed in a meticulously defined order.
The machine learning lifecycle embodies a sequential series of steps integral to data science projects. These steps form a cyclic process, each carrying significant weight and executed in a meticulously defined order.
The machine learning lifecycle embodies a sequential series of steps integral to data science projects. These steps form a cyclic process, each carrying significant weight and executed in a meticulously defined order.
MLOps
MLOps, a fusion of machine learning and operations, encompasses a suite of practices that orchestrate the deployment and maintenance of machine learning (ML) models in a production environment.
MLOps, a fusion of machine learning and operations, encompasses a suite of practices that orchestrate the deployment and maintenance of machine learning (ML) models in a production environment.
MLOps, a fusion of machine learning and operations, encompasses a suite of practices that orchestrate the deployment and maintenance of machine learning (ML) models in a production environment.
Model Monitoring
Model monitoring stands as a critical practice in the realm of machine learning (ML), focusing on the continuous assessment of ML models in production environments to preemptively identify potential issues within ML pipelines.
Model monitoring stands as a critical practice in the realm of machine learning (ML), focusing on the continuous assessment of ML models in production environments to preemptively identify potential issues within ML pipelines.
Model monitoring stands as a critical practice in the realm of machine learning (ML), focusing on the continuous assessment of ML models in production environments to preemptively identify potential issues within ML pipelines.
Natural Language Processing
Natural Language Processing (NLP), a domain within Artificial Intelligence, empowers computers to comprehend and interpret human-written languages, bridging the gap between complex computer languages and users.
Natural Language Processing (NLP), a domain within Artificial Intelligence, empowers computers to comprehend and interpret human-written languages, bridging the gap between complex computer languages and users.
Natural Language Processing (NLP), a domain within Artificial Intelligence, empowers computers to comprehend and interpret human-written languages, bridging the gap between complex computer languages and users.
Overfitting Vs. Underfitting
In the realm of machine learning, the concepts of model overfitting and underfitting hold paramount importance as they reflect the delicate equilibrium between capturing the essence of training data and generalizing well to new data.
In the realm of machine learning, the concepts of model overfitting and underfitting hold paramount importance as they reflect the delicate equilibrium between capturing the essence of training data and generalizing well to new data.
In the realm of machine learning, the concepts of model overfitting and underfitting hold paramount importance as they reflect the delicate equilibrium between capturing the essence of training data and generalizing well to new data.
Promoting Responsible AI
Responsible AI (RAI) represents a set of principles and methodologies aimed at the development, deployment, and scaling of artificial intelligence with a positive impact on both people and society.
Responsible AI (RAI) represents a set of principles and methodologies aimed at the development, deployment, and scaling of artificial intelligence with a positive impact on both people and society.
Responsible AI (RAI) represents a set of principles and methodologies aimed at the development, deployment, and scaling of artificial intelligence with a positive impact on both people and society.
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