Vector Database
A vector database is a specialized storage system optimized for storing, indexing, and querying high-dimensional vector embeddings. It powers semantic search, recommendation systems, and RAG applications.
Understanding Vector Database
A vector database is a specialized storage system designed to efficiently index, store, and retrieve high-dimensional vector embeddings at scale. Unlike traditional relational databases that match exact values, vector databases use approximate nearest neighbor algorithms to find the most similar vectors to a given query, enabling semantic search across millions or billions of records. Popular solutions include Pinecone, Weaviate, Milvus, Qdrant, and Chroma, each offering different tradeoffs between speed, accuracy, and scalability. Vector databases have become essential infrastructure for retrieval-augmented generation (RAG) systems, where relevant documents are retrieved to ground large language model responses in factual information. They also power recommendation engines, image similarity search, anomaly detection, and any application requiring fast semantic similarity comparisons across large embedding collections.
Category
AI Infrastructure
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AI Chip
An AI chip is a specialized processor designed specifically for artificial intelligence workloads like neural network training and inference. Examples include NVIDIA's GPUs, Google's TPUs, and custom ASICs.
API
An API (Application Programming Interface) is a set of protocols and tools that allows different software systems to communicate. AI APIs enable developers to integrate machine learning capabilities like text generation, image recognition, and speech processing into applications.
CUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform that allows developers to use GPUs for general-purpose processing. CUDA is the foundation of GPU-accelerated deep learning training.
Data Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format. AI systems often draw training data from data lakes that store structured, semi-structured, and unstructured information.
Data Pipeline
A data pipeline is an automated series of data processing steps that moves and transforms data from source systems to a destination. ML data pipelines handle ingestion, cleaning, feature engineering, and model training workflows.
Data Warehouse
A data warehouse is a centralized repository for structured, processed data optimized for analysis and reporting. AI and ML systems often source their training data from enterprise data warehouses.
Distributed Training
Distributed training is the practice of splitting model training across multiple GPUs or machines to handle large models and datasets. It uses data parallelism or model parallelism to accelerate training.
Edge AI
Edge AI refers to running artificial intelligence algorithms locally on hardware devices rather than in the cloud. Edge AI enables real-time inference with lower latency, better privacy, and reduced bandwidth requirements.