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.
Understanding Edge AI
Edge AI refers to the deployment and execution of artificial intelligence models directly on local devices such as smartphones, IoT sensors, cameras, and embedded systems rather than relying on cloud-based servers. By running inference at the edge, applications achieve lower latency, improved privacy, reduced bandwidth costs, and the ability to function offline. Use cases include real-time face recognition on security cameras, voice assistants on smart speakers, and predictive maintenance on factory equipment. Making models efficient enough for edge deployment often involves techniques like distillation, quantization, pruning, and the use of frozen layers to reduce computation. Hardware like NVIDIA Jetson, Google Coral, and Apple's Neural Engine are purpose-built for edge AI workloads. The growing demand for on-device intelligence continues to drive innovation in model compression and efficient neural network architectures.
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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.
Feature Store
A feature store is a centralized repository for storing, managing, and serving machine learning features. It enables feature reuse, consistency between training and serving, and collaboration across ML teams.