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.
Understanding Data Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis or model training. Unlike traditional data warehouses that require structured schemas upfront, data lakes accept structured, semi-structured, and unstructured data including text, images, logs, and sensor readings. In AI workflows, data lakes serve as the foundational layer of a data pipeline, feeding preprocessed data into feature engineering and model training stages. Cloud platforms like AWS S3, Azure Data Lake, and Google Cloud Storage provide scalable infrastructure for data lakes used by machine learning teams. Proper governance, cataloging, and access controls are essential to prevent a data lake from becoming a disorganized "data swamp" that hinders rather than helps analytical productivity.
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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 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.
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.