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.
Understanding Data Warehouse
A data warehouse is a centralized repository that stores large volumes of structured and semi-structured data collected from multiple sources, optimized for analytical queries and reporting rather than transactional processing. In the context of AI and machine learning, data warehouses serve as the foundational infrastructure for training datasets, enabling data scientists to access clean, consolidated data for model development. Modern cloud-based data warehouses like Snowflake, BigQuery, and Amazon Redshift support the massive scale required for deep learning pipelines. They work in tandem with feature stores to ensure consistent data access across training and inference environments. Effective data warehousing involves ETL processes, schema design, and data governance practices that directly impact the quality of ground truth labels and ultimately the performance of AI models in production.
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.
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.