Context Window
What is a Context Window? A context window refers to the amount of text or data a machine learning model, particularly a language model, can consider at any given time.
Conversational AI
What is Conversational AI? Conversational AI refers to artificial intelligence technologies that enable machines to engage in human-like interactions through text or speech. It powers chatbots, virtual assistants, and voice-based
Cost Allocation Tags
Cost allocation tags are key-value pairs attached to AWS resources that allow organizations to categorize and track their AWS costs with high granularity. These tags enable organizations to assign costs
Cost Anomaly Detection
Cost anomaly detection utilizes machine learning to identify and alert to unusual spending patterns within an organization’s AWS services. This tool is essential for monitoring and managing cloud costs by
Data Augmentation
Data augmentation is generating new data samples by modifying existing data. It helps improve machine learning (ML) model performance by increasing data variety without collecting more real-world data. The process
Data Labeling
What is Data Labeling? Data labeling is tagging or annotating raw data—such as images, text, audio, or video—with meaningful labels that allow machine learning models to understand and learn from
Data Privacy in AI
What is Data Privacy in AI? Data privacy in artificial intelligence refers to protecting personal, sensitive, or confidential information used to train, test, or operate AI systems. As AI models
Data Transfer Costs
Data transfer costs in cloud computing refer to the fees associated with moving data within and between cloud services, across different regions, or from the cloud to on-premises environments. These
Deep Learning
Deep learning is a subset of machine learning that uses algorithms modeled after the human brain’s neural networks. It enables computers to analyze and learn from large amounts of data,
Differential Privacy
What Is Differential Privacy? Differential privacy is a mathematical framework that protects individual data while allowing functional analysis of large datasets. It ensures that removing or adding a single data
Diffusion Models
Diffusion models are generative machine learning models that create data, such as images, text, or audio, by reversing a noise process. They learn to generate high-quality outputs by gradually denoising
Digital Twin AI
What is Digital Twin AI? Digital Twin AI is a technology that creates a virtual model of a physical object, system, or process. It continuously updates using real-time sensor data,
Dynamic Provisioning
Dynamic provisioning in cloud computing and data centers refers to the automated process of allocating and managing storage resources on demand. This technology eliminates the need for administrators to manually
Edge AI
What is Edge AI? Edge AI is artificial intelligence that processes data directly on local devices rather than on centralized cloud servers. This approach allows AI to function in real
Egress Charges
Egress charges refer to the fees incurred when data is transferred from a cloud provider’s network to another location, such as another cloud service, an on-premises data center, or the
Elasticity
Elasticity in cloud computing refers to the ability of a cloud environment to dynamically allocate and de-allocate resources as needed to handle fluctuating workloads efficiently. This capability allows systems to
Embeddings
Embeddings are a technique used in machine learning and natural language processing (NLP) to represent data—especially words, sentences, or items—as numerical vectors. These vectors capture the relationships, context, and similarities
Explainable AI (XAI)
What Is Explainable AI (XAI)? Explainable AI (XAI) refers to artificial intelligence systems that make their decision-making processes transparent. Unlike traditional AI models that work like black boxes, XAI provides
Federated Learning
What is Federated Learning? Federated learning is a machine learning technique allowing multiple devices or organizations to train a shared model collaboratively without exchanging the underlying data. Unlike traditional centralized
Few-Shot Learning
Few-shot learning is a type of machine learning where a model learns to make accurate predictions using only a small number of labeled examples. Unlike traditional machine learning, which requires
