Glossary of Cloud AI Terms

Cloud AI and machine learning has become inundated with technical jargon and industry buzzwords. The LogicalCube Cloud AI Glossary is your guide through this confusing technical terminology, providing you clear definitions of cloud AI and ML terms.

Artificial Intelligence (AI): The simulation of human intelligence in machines that can perform tasks requiring human-like cognitive abilities.

AutoML: Automated Machine Learning, where ML tasks such as feature selection, model training, and hyperparameter tuning are automated to streamline the ML workflow.

Bias in AI: Unfair or discriminatory outcomes caused by biases present in the data, algorithms, or decision-making processes used in AI systems.

Cloud Computing: The delivery of on-demand computing resources, including storage, processing power, and applications, over the internet, without the need for local infrastructure.

Cloud AI/ML Services: Pre-built AI/ML tools and services offered by cloud providers, allowing users to leverage powerful AI capabilities without the need for extensive infrastructure setup or ML expertise.

Computer Vision: A field of AI focused on enabling computers to understand and interpret visual information from images or videos, often used in applications like object recognition and autonomous driving.

Data Labeling: The process of annotating or tagging data with specific labels or tags to create labeled datasets for supervised ML training.

Data Preprocessing: The process of cleaning, transforming, and organizing raw data to make it suitable for ML algorithms to learn from.

Deep Learning: A subfield of ML that uses artificial neural networks to learn and make complex decisions, often used for tasks such as image recognition and natural language processing.

Elasticity: The ability of cloud resources to dynamically scale up or down based on demand, allowing users to adjust computing resources as needed.

Explainable AI: The ability to provide explanations or justifications for the predictions or decisions made by AI systems, increasing transparency and trust.

Feature Extraction: The process of selecting and extracting relevant features from raw data to represent patterns or characteristics that can be used as input for ML models.

Hyperparameters: Parameters that are set before the training process and control the behavior and performance of a ML model, such as learning rate and regularization strength.

Infrastructure as a Service (IaaS): A cloud computing model where virtualized computing resources, such as virtual machines and storage, are provided to users over the internet.

Machine Learning (ML): A subset of AI that enables machines to learn from data and improve their performance without explicit programming.

Model Deployment: The process of integrating a trained ML model into an operational system or application, making it available for predictions or decision-making.

Model Evaluation: The process of assessing the performance and accuracy of a trained ML model using appropriate metrics and validation techniques.

Model Inference: The process of using a trained ML model to make predictions or generate outputs based on new, unseen data.

Model Monitoring: Continuous monitoring and evaluation of deployed ML models to ensure their performance, accuracy, and fairness over time.

Natural Language Processing (NLP): The ability of computers to understand, interpret, and generate human language, enabling tasks such as text analysis, sentiment analysis, and language translation.

Neural Network: A network of interconnected artificial neurons that work together to process and analyze data, mimicking the structure of the human brain.

Overfitting: A condition in ML where a model performs exceptionally well on the training data but fails to generalize well on unseen or new data.

Platform as a Service (PaaS): A cloud computing model where a platform for developing, deploying, and managing applications is provided to users, removing the need to manage underlying infrastructure.

Reinforcement Learning: A type of ML where an agent learns through trial and error by interacting with an environment and receiving rewards or punishments based on its actions.

Software as a Service (SaaS): A cloud computing model where software applications are provided to users over the internet, eliminating the need for installation and maintenance on local devices.

Supervised Learning: A type of ML where the model is trained using labeled data, allowing it to make predictions or classifications based on patterns learned from the training examples.

Transfer Learning: A technique in ML where knowledge learned from one task or domain is transferred to another related task or domain, reducing the need for extensive training data.

Underfitting: A condition in ML where a model fails to capture the underlying patterns in the training data, resulting in poor performance.

Unsupervised Learning: A type of ML where the model learns from unlabeled data, identifying patterns and structures in the data without explicit guidance.

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